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Studying Traffic Safety During the Transition Period Between Manual Driving and Autonomous Driving: A Simulation-Based Approach
Abstract
Connected and Autonomous Vehicles (CAVs) are becoming a reality and are progressively penetrating the markets level by level. CAVs are a promising solution for traffic safety. However, robust studies are needed to explore and assess the expected behavior. This study attempts to evaluate traffic safety resulting from a near-real introduction of CAVs with different levels of automation (from Level 1 to Level 4). The investigation consisted of modeling different CAV levels using Gipps’ model, followed by the simulation of nine mixed fleets at a motorway segment. Subsequently, the Surrogate Safety Assessment Model was used for safety analysis. According to the results: (1) the gradual penetration of CAV levels led to a progressive reduction in traffic conflicts, ranging from 18.9% when the penetration of high levels of automation (Level 3 and Level 4 vehicles) is 5%, to 94.1% when all the vehicles on the traffic flow are Level 4; (2) human-driven vehicles and vehicles with low levels of automation (Level 1 and Level 2 vehicles) are more frequently involved in conflicts (as follower vehicles) than vehicles with high automation levels. E.g. human-driven vehicles are involved in conflicts from 8% to 122% more, while vehicles with high automation levels are involved in conflicts from 80% to 18% less than their sharing percentages, respectively, depending on different mixed fleets. This study confirms the theory and conclusions from previous literature that indicate a safety gain due to CAV penetration. Moreover, it provides a broader perspective and support for the introduction of CAVs levels.
... As a case study, the motorway segment of 20.27 km that was modelled in Miqdady et al. (2023) is reconfigured with a DL to be exposed to comparison with the mentioned study's results as a base condition (0 DL). Regarding the modelling criteria for deploying the dedicated lane, the following policies are applied to be tested: ...
... Furthermore, based on data acquired from several detectors set along the route by the General Traffic Direction (Dirección General de Tráfico, DGT), the network information (volume counts, speeds, traffic composition (passenger cars vs. heavy vehicles), etc.) was defined regarding both peak and off-peak conditions by direction (northbound and southbound) to calibrate and validate the modelled motorway traffic operations. See Miqdady et al. (2023) for more details about the criteria used for the calibrated volumes and the validation of the modelled network. ...
... As well, the levels of automation (SAE, 2014) and connectivity were handled (see Miqdady et al. 2023) by calibrating the parameters of Gipps' traffic models (used in Aimsun) and V2X extension before applying various suggested fleet mixed scenarios displayed in Table 1. Table 1 exhibits the composition of each scenario by the percentage of vehicle type: human-driven vehicle (HDV), and the levels of automation from Level 1 to Level 4. The potential percentage of vehicles on the dedicated lane (i.e. ...
... They calibrated all the model parameters' values simultaneously among different traffic demands and did not specifically investigate the impact of changing these parameters. Similarly, Miqdady et al. [16,17] proposed various values for Gipps' car-following and lane-change models among automation levels and vehicle types (passenger car, heavy vehicle). However, like [15], they ran the various calibrated values simultaneously without exploring the impact of changing these parameters. ...
... The generated traffic operations were validated in terms of volume and speed by vehicle type (pc, hv) and for 15 min intervals following the Roads and Maritime Services modelling guidelines [34]. For more details about the validation process, refer to our previous analysis [16]. A preliminary analysis [26] was conducted to assign a statistically sufficient number of runs based on Shahdah et al.'s [35] equation, which was found to be 15 runs. ...
... For the rest of the combinations with anything other than 1 m/s² maximum acceleration, a gradual improvement in traffic safety was registered by decreasing the reaction time and increasing the maximum acceleration. Only two studies have considered these two parameters at the same time ( [16,22]). In [22], a low value of acceleration (1 m/s²) was combined with a 0.5 s reaction time, and their results varied between deteriorating and enhancing traffic safety on the road. ...
Recently, the number of traffic safety studies involving connected and autonomous vehicles (CAVs) has been increasing. Due to the lack of information regarding the real behaviour of CAVs in mixed traffic flow, traffic simulation platforms are used to provide a reasonable approach for testing various scenarios and fleets. It is necessary to analyse how traffic safety is affected when key parameter assumptions are changed. The current study conducts a sensitivity analysis to identify the parameters used in CAV calibration that have the highest influence on traffic safety. Using a microsimulation-based surrogate safety assessment model approach (SSAM), traffic conflicts were identified, and a ceteris paribus analysis was conducted to measure the effect of gradually changing each parameter on the number of conflicts. Afterwards, a two-at-a-time sensitivity analysis was performed to explore the influence of simultaneously varying two parameters. The results revealed that reaction time, clearance, maximum acceleration, normal deceleration, and the sensitivity factor are key parameters. Studying these parameters two at a time revealed that low maximum acceleration, when combined with other parameters, consistently resulted in the highest number of conflicts, while combinations with short reaction time always yielded the best traffic safety results. This investigation broadens the understanding of CAV behaviour for future implementation for both manufacturers and researchers.
... In most previous studies, CAVs typically have a high automation level (i.e., L4) [3][4][5][6][7]. However, other studies have also included several levels of automation [8][9][10]. In general, they found that increasing the penetration rates of CAV can signifcantly reduce the number of potential conficts. ...
... Furthermore, the model operations were calibrated and validated following the modeling guidelines of Roads and Maritime Services [56]. Miqdady et al. [10] provide more details about this step. ...
... Te analysis attempted to cover a gradually introduction of CAVs with various feet mixes that the real world may encounter. Accordingly, as justifed in Miqdady et al. [10], nine mixed feet scenarios with diferent CAV penetration rates were suggested. Table 2 lists the combinations of HDVs and vehicles with diferent automation levels (L1-L4) in each scenario. ...
This study aims to estimate the severity of conflicts that may arise from the introduction of connected and automated vehicles (CAVs) by examining the vehicle paths generated by microsimulations of mixed fleets of human-driven vehicles and CAVs with different levels of automation (L1-L4 vehicles). The study assesses the severity of conflicts using a holistic approach that considers three dimensions: (1) proximity to collision, via the time-to-collision (TTC) indicator; (2) potential consequences of a conflict, via single surrogate safety measures such as maximum speed (MaxS) and vehicle speed difference (DeltaS); and (3) a combination of both dimensions to assign severity scores, via TTC and velocity vectors. The study’s findings suggest that moderate penetration rates of L3 and L4 vehicles (35–55%) show significant differences in the number of traffic conflicts with varying TTC values. Additionally, high penetration rates of L3 and L4 vehicles (above 55%) result in lower values of conflict consequences measures such as MaxS and DeltaS. Furthermore, the study shows that conflict consequences decrease if the follower is a L3 or L4 vehicle. The study’s findings also reveal that there is a considerable reduction in high severity conflicts when the penetration rate of CAV levels reaches 50%, and the full operation of L4 vehicles results in a 75.5% reduction in high severity conflicts. Therefore, this study provides valuable insight into the potential severe conflicts during the transition period from manual vehicle operation to full CAV operation. Overall, the study’s findings highlight the importance of assessing the severity of potential conflicts arising from the introduction of CAVs. By considering the proximity to collision and the potential consequences of conflicts, the study provides a comprehensive assessment of the severity of conflicts. This information can inform the development of policies and strategies to ensure the safe and responsible introduction of CAVs into our transportation systems.
The gradual deployment of Connected and Automated Vehicles (CAV) in traffic will result in a transition period in which vehicles with various levels of automation and connectivity will have to co-exist with non-connected and non-automated road users for quite some time. Consequently, new types of interactions will emerge (and old types of interactions are likely to become more complicated) between vehicles at different levels of automation and other road users which could have significant implications on traffic safety and efficiency. Understanding the nature of these interactions, how humans might adapt their behavior, how connectivity can be utilized to proactively enhance drivers char63 driving performance, and how automated vehicles can be programmed to behave in different driving situations to guarantee safety and efficiency remain among the key knowledge gaps that require scientific research. This knowledge is crucial for the development of adequate integration policies of connected and automated vehicles in mixed traffic environment, for updating and improving automated vehicles char63 algorithms and software, for designing the physical and digital road infrastructure, and for operating and managing traffic on the road network.
Connected Vehicle (CV) notifications enhance drivers' situational awareness, where, depending on traffic flow characteristics, CV drivers might require sufficient distance and time to adapt their driving behavior accordingly. Hereof, in addition to CVs Market Penetration Rate (MPR) and traffic flow characteristics, humans are still in the safety loop. Hence, there might be a human-related unobserved heterogeneity affecting the safety performance of CVs. A conflation of microsimulation and advanced statistical modeling was followed to account for this unobserved heterogeneity by investigating the random effect of varying delivery distances, specifically provided for the Work Zone Warning (WZW) application. CVs behaviors were calibrated based on the results of driving simulator experiments. A typical WZ was simulated under VISSIM using three levels of low, moderate, and high traffic volumes. Five levels of CV-MPR received the WZW on four different settings of delivery distances, including 1500, 3000, 4500, and 6000 ft, upstream of the Transition Area. Accordingly, 60 scenarios were simulated, and the number of critical Time-to-Collision (TTC) was considered as the response count variable per scenario. Four hierarchical Negative Binomial regressions under Bayesian inference were conducted on 60 observations over four WZ areas, where the delivery distance was treated as a random factor. Analysis of the Intraclass Correlation Coefficient showed that the number of observed critical TTCs is nested within the delivery distances. It was ascertained, at any CV-MPR, when the WZW delivery distance increases, the number of critical TTCs decreases that indicates the same safety performance at higher CV-MPR can be achieved even at lower MPR if the WZW distancing increases. Besides, the optimal Road Side Unit (RSU) placement for WZW was found to be 4500 ft upstream of the Transition Area regardless of traffic volume and CV-MPR. Although this study was restricted to the CV-WZW application, the introduced delivery distance for CV notifications should be regarded in future studies as a contributing factor to the safety assessment of other CV applications.
Managed lanes, such as a dedicated lane for connected and automated vehicles (CAVs), can provide not only technological accommodation but also desired market incentives for road users to adopt CAVs in the near future. In this paper, we investigate traffic flow characteristics with two configurations of the managed lane across different market penetration rates and quantify the benefits from the perspectives of lane-level headway distribution, fuel consumption, communication density, and overall network performance. The results highlight the benefits of implementing managed lane strategies for CAVs: (1) A dedicated CAV lane significantly extends the stable region of the speed-flow diagram and yields a greater road capacity. As the result shows, the highest flow rate is 3400 vehicles per hour per lane at 90% market penetration rate with one CAV lane. (2) The concentration of CAVs in one lane results in a narrower headway distribution (with smaller standard deviation) even with partial market penetration. (3) A dedicated CAV lane is also able to eliminate duel-bell-shape distribution that is caused by the heterogeneous traffic flow. (4) A dedicated CAV lane creates a more consistent CAV density, which facilitates communication activity and decreases the probability of packet dropping.
Road traffic signals and traffic infrastructure have a significant impact on the behaviour of highly automated or autonomous vehicles. However, the increase in automation does not always mean an advantage. Generally, highly automated vehicles strictly follow the traffic rules resulting in near-accident situations, although their goal is to avoid and reduce them. Malfunctions of the automated functions might cause surprising interventions while the vehicle is in motion, drivers cannot react in time and well. This paper highlights the potential danger and uncertainty of highly automated or autonomous vehicles in the context of the current conventional traffic infrastructure system. In the future, special consideration shall be given to the vehicle industry and traffic regulation makers on how the infrastructure should be adapted to automated vehicle functions to have a seamless shift towards automated driving. The paper sums up many problematic situations with two critical problems with sensitivity analysis. The first situation: the speed assist system (based on speed limit sign recognition) conflicts with the traffic infrastructure. The second situation is shown: the ACC (Adaptive Cruise Control) and LKA (Lane Assist) contradicts with the traffic infrastructure. These critical situations were investigated by using an high-fidelity automotive simulation software as proof of concept and were examined by accident reconstruction analysis software.
Autonomous Vehicles (AVs) have a large potential to improve traffic safety but also pose some critical challenges. While AVs may help reduce crashes caused by human error, they still may experience failures of technologies and sensing, as well as decision-making errors in a mixed traffic environment. A disengagement refers to an AV transitioning control from autonomous systems to the trained test driver. The safety critical nature of disengagements makes it imperative to analyze disengagements and crashes together. This study analyzes both crashes and disengagements from real-world AV driving in California to fill the knowledge gap regarding the relationship between disengagements and crashes in a mixed traffic environment. A nested logit model was calibrated using three different outcomes: (1) disengagement with a crash, (2) disengagement with no crash, and (3) no disengagement with a crash. Furthermore, endogenous switching regime models were also calibrated to draw distinctions between the relation of disengagements and crashes while accounting for endogeneity effects. The results show that factors related to AV systems (such as software failures) and other roadway participants increase the propensity of a disengagement without a crash. Furthermore, AVs were observed to disengage less often as the technology matured over time. Marginal effects revealed an 8% decrease. The results thus suggest that disengagements are a part of AVs' safe performance and disengagement alerts may need to be triggered in order to avoid certain failures with current technology.
Connected and Autonomous Vehicles (CAVs) are expected to bring major transformations to transport efficiency and safety. Studies show a range of possible impacts, from worse efficiency of CAVs at low penetration rates, to significant improvements in both efficiency and safety at high penetration rates and loads. However, these studies tend to explore efficiency and safety separately, focus on one type of a road network, and include only cars rather than other vehicle types. This paper presents a comprehensive study on impact of CAVs on both efficiency and safety, in three types of networks (urban, national, motorway), simulating different penetration rates of vehicles with multiple levels of automation, using historical traffic data captured on Irish roads. Our study confirms existing results that near-maximum efficiency improvements are observed at relatively low penetration rates, but reveals further insights that the exact penetration ranges between 20% and 40% depending on the network type and traffic conditions. Safety results show a 30% increase of conflicts at lower penetration rates, but 50-80% reduction at higher ones, with consistent improvement for increased penetration. We further show that congestion has a higher impact on conflicts than penetration rates, highlighting the importance of unified evaluation of efficiency and safety.
Plenty of studies on exclusive lanes for Connected and Autonomous Vehicle (CAV) have been conducted recently about traffic efficiency and safety. However, most of the previous research studies neglected comprehensive consideration of the safety impact on different market penetration rates (MPRs) of CAVs, traffic demands, and proportion of trucks in mixture CAVs with human’s driven vehicle environment. On this basis, this study is to (1) identify the safety impact on exclusive lanes for CAVs under different MPRs with different traffic demands and (2) investigate the safety impact of trucks for CAV exclusive lanes on mixture environment. Based on the Intelligent Driver Model (IDM), a CAV platooning control algorithm is proposed for modeling the driving behaviors of CAVs. A calibrated 7-kilometer freeway section microscopic simulation environment is built by VISSIM. Four surrogate safety measures, including both longitudinal and lateral safety risk indexes, are employed to evaluate the overall safety impacts of setting exclusive lanes. Main results indicate that (1) setting one exclusive lane is capable to improve overall safety environment in low demand, and two exclusive lanes are more suitable for high-demand scenario; (2) existence of trucks worsens overall longitudinal safety environment, and improper setting of exclusive lanes in high trucks, low MPR scenario has adverse effect on longitudinal safety; and (3) setting exclusive lanes have better longitudinal and lateral safety improvement in high-truck proportion scenarios. Setting one or two exclusive lanes led to [+42.4% to −52.90%] and [+45.7% to −55.2%] of longitudinal risks while [−1.8% to −87.1%] and [−2.1% to −85.3%] of lateral conflicts compared with the base scenario, respectively. Results of this study provide useful insight for the setting of exclusive lanes for CAVs in a mixture environment.
Driving experiences provided by the introduction of new vehicle technologies are directly impacting the criteria for road network design. New criteria should be taken into consideration by designers, researchers and car owners in order to assure traffic safety in changed conditions that will appear with, for example, introduction of Autonomous Vehicles (AVs) in everyday traffic. In this paper, roundabout safety level is analysed on the originally developed microsimulation model in circumstances where different numbers of AVs vehicles are mixed with Conventional Vehicles (CVs). Field data about speed and traffic volumes from existing roundabouts in Croatia were used for development of the model. The simulations done with the Surrogate Safety Assessment Model (SSAM) give some relevant highlights on how the introduction of AVs could change both operational and safety parameters at roundabouts. To further explore the effects on safety of roundabouts with the introduction of different shares of AVs, hypothetical safety treatments could be tested to explore whether their effects may change, leading to the estimation of a new set of Crash Modification Factors.
Autonomous Vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from VISSIM traffic micro-simulator using Surrogate Safety Assessment Model (SSAM). Behaviours of Human-driven Vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car following model. The safety investigation is conducted for two case studies, including a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at p<0.05). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at p<0.05).
Autonomous vehicles offer a wide variety of potential benefits. One commonly discussed benefit is improved traffic operations (that is, decreased congestion, decreased delay, and improved efficiency) due to the way that autonomous vehicles are expected to behave in a traffic stream. In this research, we evaluate the effect of varying the percentage of autonomous vehicles in the overall vehicle fleet mix on transportation network performance.
This paper presents a new experimental approach to simulate projected autonomous driving styles based on the accelerations at three road profiles. This study was focused on the determination of ranges of accelerations in triaxial direction to simulate the autonomous driving experience. A special device, known as the Automatic Acceleration and Data controller (AUTOAccD), has been developed to guide the designated driver to accomplish the selected accelerations based on the road profiles and the intended driving styles namely assertive, defensive and light rail transit (LRT). Experimental investigations have been carried out at three different road profiles (junction, speed hump, and corner) with two designated drivers with five trials on each condition. A driving style with the accelerations of LRT has also been included in this study as it is significant to the present methodology because the autonomous car is predicted to accelerate like an LRT, in such a way that it enables the users to conduct activities such as working on a laptop, using personal devices or eating and drinking while travelling. The results demonstrated that 92 out of 110 trials of the intended accelerations for autonomous driving styles could be achieved and simulated on the real road by the designated drivers. The differences between the two designated drivers were negligible, and the rates of succeeding in realizing the intended accelerations were high. The present approach in simulating autonomous driving styles focusing on accelerations can be used as a tool for experimental setup involving autonomous driving experience and acceptance.
The primary objective of this paper is to provide a statistical relationship between traffic conflicts estimated from microsimulation and observed crashes in order to evaluate safety performance, in particular the effect of countermeasures. A secondary objective is to assess the effect of conflict risk tolerance and number of simulation runs on the estimates of countermeasure effects so obtained. Conflicts were simulated for a sample of signalized intersections from Toronto, Canada, using VISSIM microscopic traffic simulation and several crash–conflict relationships were obtained. A separate sample of treated intersections from Toronto was used to compare countermeasure effects from the integrated crash–conflict expression to a conventional, but rigorous crash-based Empirical Bayes before-and-after analysis that was already done, with the results published, for the same sites and treatment. The countermeasure considered for this investigation involved changing the left turn signal operation for the treated intersection sample from permissive to protected-permissive. The results support the view that countermeasure effects can be estimated reliably from conflicts derived from microsimulation, and more so when a suitable number of simulation runs and conflict tolerance thresholds are used in the crash–conflict relationship.
Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to our transportation system. This new technology has the potential to impact vehicle safety, congestion, and travel behavior. All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach $2000 to per year per AV, and may eventually approach nearly $4000 when comprehensive crash costs are accounted for. Yet barriers to implementation and mass-market penetration remain. Initial costs will likely be unaffordable. Licensing and testing standards in the U.S. are being developed at the state level, rather than nationally, which may lead to inconsistencies across states. Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm. The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain. To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy.
Various wireless communication systems exist, which enable a wide range of applications and use cases in the vehicular environment. These applications can be grouped into three types, namely, road safety, traffic efficiency, and infotainment, each with its own set of functional and performance requirements. In pursuance of assisting drivers to travel safely and comfortably, several of these requirements have to be met simultaneously. While the coexistence of multiple radio access technologies brings immense opportunities towards meeting most of the vehicular networking application requirements, it is equally important and challenging to identify the strength and weaknesses of each technology and understand which technology is more suitable for the given networking scenario. In this paper, we evaluate two of the most viable communication standards, Institute of Electrical and Electronics Engineers (IEEE) 802.11p and long-term evolution (LTE) by 3rd Generation Partnership Project for vehicular networking. A detailed performance evaluation study of the standards is given for a variety of parameter settings such as beacon transmission frequency, vehicle density, and vehicle average speed. Both standards are compared in terms of delay, reliability, scalability, and mobility support in the context of various application requirements. Furthermore, through extensive simulation-based study, we validated the effectiveness of both standards to handle different application requirements and share insight for further research directions. The results indicate that IEEE 802.11p offers acceptable performance for sparse network topologies with limited mobility support. On the other hand, LTE meets most of the application requirements in terms of reliability, scalability, and mobility support; however, it is challenging to obtain stringent delay requirements in the presence of higher cellular network traffic load.
Modeling and simulation tools are being increasingly acclaimed in the research field of autonomous vehicles systems, as they provide suitable test beds for the development and evaluation of such complex systems. However, these tools still do not account for some integration capabilities amongst several state-of-the-art Intelligent Transportation Systems, e.g. to study autonomous driving behaviors in human-steered urban traffic scenarios, which are crucial to the Future Urban Transport paradigm.
In this paper we describe the modeling and implementation of an integration architecture of two types of simulators, namely a robotics and a traffic simulator. This integration should enable autonomous vehicles to be deployed in a rather realistic traffic flow as an agent entity (on the traffic simulator), at the same time it simulates all its sensors and actuators (on the robotics counterpart). Also, the statistical tools available in the traffic simulator will allow practitioners to infer what kind of advantages such a novel technology will bring to our everyday's lives. Furthermore, an architecture for the integration of the aforementioned simulators is proposed and implemented in the light of the most desired features of such software environments.
To assess the usefulness of the platform architecture towards the expected realistic simulation facility, a comprehensive system evaluation is performed and critically reviewed, leveraging the feasibility of the integration. Further developments and future perspectives are also suggested.
Automated vehicle (AV) technologies are fast, new, and have been introduced to create transport systems that enhance road safety and mobility. However, evaluating their safety has been difficult due to a lack of AV exposure data. This paper investigates the safety impact of AVs using a simulation-based surrogate safety-measure approach. Three measures of safety were chosen: time-to-collision (TTC) distribution, post-encroachment time (PET), and conflict points. The conflict-point output was also used to estimate the number of crashes per year.
This paper’s results could provide useful insights for network operators about traffic conditions and road safety after the implementation of AVs. The results show that AVs improve safety; the number of crashes was reduced by about 12%.
Connected and Autonomous Vehicles (CAV) is the developing summit of the integration between artificial intelligence (AI), robotics, automotive design and information technologies. Many researchers are investigating their effects on traffic safety. This study tries to quantify the volume of incidents when sharing the road human-driven vehicles and fully CAV. After modeling the geometry of 4.5 km of motorway and the parameters of connectivity and automation using Aimsun Next platform, several scenarios of the percentages of CAV (0%, 25%, 50%, 75%, and 100%) were driven in microsimulation runs. Then the microsimulation generated vehicles trajectories that are used to identify conflicts using the Surrogate Safety Assessment Model (SSAM). The results of this analysis confirm previous research in that the reduction of number of conflicts will be up to 35% with low and moderate penetration rates of CAV and more than 80% if the road is operated only with CAV.
Surrogate Safety Measures (SSM) are important for safety performance evaluation, since crashes are rare events and historical crash data does not capture near crashes that are also critical for improving safety. This paper focuses on SSM and their applications, particularly in Connected and Automated Vehicles (CAV) safety modeling. It aims to provide a comprehensive and systematic review of significant SSM studies, identify limitations and opportunities for future SSM and CAV research, and assist researchers and practitioners with choosing the most appropriate SSM for safety studies. The behaviors of CAV can be very different from those of Human-Driven Vehicles (HDV). Even among CAV with different automation/connectivity levels, their behaviors are likely to differ. Also, the behaviors of HDV can change in response to the existence of CAV in mixed autonomy traffic. Simulation by far is the most viable solution to model CAV safety. However, it is questionable whether conventional SSM can be applied to modeling CAV safety based on simulation results due to the lack of sophisticated simulation tools that can accurately model CAV behaviors and SSM that can take CAV’s powerful sensing and path prediction and planning capabilities into crash risk modeling, although some researchers suggested that proper simulation model calibration can be helpful to address these issues. A number of critical questions related to SSM for CAV safety research are also identified and discussed, including SSM for CAV trajectory optimization, SSM for individual vehicles and vehicle platoon, and CAV as a new data source for developing SSM.
In the foreseeable future, connected vehicles (CVs) will coexist with traditional vehicles (TVs) resulting in a complex mixed traffic environment and the success of CVs will depend on the characteristics of this mixed traffic. Therefore, before the large scale deployment of CVs, it is important to examine how CVs will influence the characteristics of the resultant mixed traffic. To achieve this aim, this study models the mixed traffic of TVs and CVs, and examines the traffic flow disturbance, efficiency, and safety. Intelligent Driver Model (IDM) with estimation errors is utilised to model TVs since it incorporates human factors such as estimation errors. Whereas, connected vehicle driving strategy integrated with IDM is utilised to model CVs because it incorporates driver compliance, a critical human factor for the success of CVs. Moreover, two classes of drivers based on their compliance levels are considered, namely the high-compliance drivers and the low-compliance drivers, to comprehensively investigate the impact of driver compliance on the mixed traffic of CVs and TVs. Two simulation experiments are performed in this study. The first experiment is used to measure traffic flow disturbance and safety while the second is used to measure the traffic flow efficiency. Furthermore, a total of 7 mixed traffic environments are generated in each experiment via different combinations of TVs, CVs with low compliance drivers, and CVs with high compliance drivers. Another important point considered in the simulation is the spatially distribution of CVs in the platoon. As such, three platoon policies are investigated. In the first policy i.e., the best case, the CVs are spatially arranged with a motive to maximise benefits from CVs whereas in the second policy i.e., the worst case, the CVs are spatially arranged with a motive to minimise benefits from CVs. Finally, in the third platoon policy i.e., the random case, the CVs are distributed randomly in the platoon. This study demonstrates the importance of the spatial arrangement of CVs in a platoon at a given penetration rate and its impact on traffic flow disturbance, efficiency, and safety. Moreover, findings from this study underscores that CVs can supress the traffic flow disturbance, and enhance traffic flow efficiency, and safety; however, traffic engineers and policy makers have to be cautious regarding how CVs are distributed in a traffic stream when deploying these vehicles in the real world traffic environment.
Autonomous vehicles (AVs) have been the subject of extensive research in recent years and are expected to completely transform the operation of transport networks and revolutionize the automotive industry in the coming decades. Modeling detailed interactions among vehicles with varying levels of penetration rates is essential for evaluating the potential effects. One such investigation is being performed within the ‘HumanDrive’ Project in the U.K. This work has required the development of a behavioral model that incorporates microscopic level interactions and has been based on a pre-existing adaptive cruise control and lane-changing model that has been adapted to better replicate the limitations of AVs and allow the investigation of differing levels of intelligence or assertiveness. The model has been implemented on the M1 Motorway near Sheffield in the U.K. This has allowed the investigation of the effects of AVs on the operation of a real network under various traffic conditions where the overall effects may be revealed, both as advantages to AV drivers, and potentially disadvantages to non-AV traffic. Additionally, it has been possible to examine how these affect junction operations and net emissions. Preliminary results have allowed us to quantify the positive effects of AVs which increase with the penetration. However, it is clear that there are points of inflection where benefits start to slow. It is at these (high) penetration rates that initial operational assumptions may become increasingly stretched and additional infrastructure and cooperative systems are likely to have to become prevalent.
We use traffic simulations to quantify the impact of autonomous vehicles in various traffic scenarios, where vehicles at higher automation levels behave more opportunistically in car-following and lane-changing and can react to road situations more quickly. Our experimental results show that an increased automation level can improve traffic efficiency but may lead to more potential conflicts between vehicles, which should not be neglected if human drivers still need to take part in the driving.
The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle's driving behaviour will no longer be solely based on the driver's limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications.
This paper aims to investigate the safety impact of connected vehicles and connected vehicles with the lower level of automation features under vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) communication technologies. Examining the lower level of automation is more realistic in the foreseeable future. This study considered two automated features such as automated braking and lane keeping assistance which are widely available in the market with low penetration rates. Driving behavior of connected vehicles (CV) and connected vehicles lower level automation (CVLLA) were modeled in the C++ programming language with considering realistic car following models in VISSIM. To this end, safety impact on both segment and intersection crash risks were explored through surrogate safety assessment techniques under various market penetration rates (MPRs). Segment crash risk was analyzed based on both time proximity-based and evasive action-based surrogate measures of safety: time exposed time-to-collision (TET), time integrated time-to-collision (TIT), time exposed rear-end crash risk index (TERCRI), lane changing conflicts (LCC), and number of critical jerks (NCJ). However, the intersection crash risk was evaluated through the number of conflicts extracted from micro-simulation (VISSIM) using the Surrogate Safety Assessment Model (SSAM). A logistic regression model was also developed to quantify the crash risk in terms of observed conflicts obtained in the intersection influence areas. The results suggest that both CV and CVLLA reduce segment crash risk significantly in terms of the five surrogate measures of safety. Furthermore, the logistic regression results clearly showed that both CV and CVLLA have lower intersection crash risks compared to the base scenario. In terms of both segment and intersection crash risks, CVLLA significantly outperforms CV when MPRs are 60% or higher. Thus, the results indicate a significant safety improvement resulting from implementing CV and CVLLA technologies at both segments and intersections on arterials.
Connected and automated vehicles (CAVs) show great potential to improve both traffic efficiency and safety by sharing information. This paper addresses the problem of coordinating two strings of vehicles at highway on-ramps efficiently and safely in the longitudinal direction. A rule-based adjusting algorithm is proposed to achieve a near-optimal merging sequence for vehicles coming from the mainline and entering through the ramp. Optimality analysis indicates that the proposed method performs very well compared with the global optimal solutions. Furthermore, to investigate the effectiveness and robustness of the proposed method, simulation-based case studies are carried out under both balanced and unbalanced scenarios. The results are compared with two other control strategies (i.e., rule-based methods and optimization-based methods) in terms of throughput, delay, computational cost, and fuel consumption.
The transportation network can provide additional utility by addressing the safety concerns on roads. On-road fatalities are an unfortunate loss of life and lead to significant costs for society and the economy. Connected and Autonomous Vehicles (CAVs), envisaged as operating with idealised safety and cooperation, could be a means of mitigating these costs. This paper intends to provide insights into the safety improvements to be attained by incrementally transitioning the fleet to CAVs. This investigation is done by constructing a calibrated microsimulation environment in Vissim and deploying the custom developed Virdi CAV Control Protocol (VCCP) algorithm for CAV behaviour. The CAV behaviour is implemented using an application programming interface and a dynamic linking library. CAVs are introduced to the environment in 10% increments, and safety performance is assessed using the Surrogate Safety Assessment Module (SSAM). The results of this study show that CAVs at low penetrations result in an increase in conflicts at signalised intersections but a decrease at priority-controlled intersections. The initial 20% penetration of CAVs is accompanied by a +22%, −87%, −62% and +33% change in conflicts at the signalised, priority, roundabout and DDI intersection respectively. CAVs at high penetrations indicate a global reduction in conflicts. A 90% CAV penetration is accompanied by a −48%, −100%, −98% and −81% change in conflicts at the signalised, priority, roundabout and DDI intersection respectively.
This study aims to analyze the impact of connected and autonomous vehicles (CAVs) on traffic safety under various penetration rates. Based on a recently proposed heterogeneous flow model, the mixed traffic flow with both conventional vehicles and CAVs was simulated and studied. The frequency of dangerous situations and value of time-to-collision in the mixed traffic flow under different CAV penetration rates was calculated and used as indicators of CAV’s impact on traffic safety. Acceleration rate and velocity difference distribution of the mixed traffic flow was presented to show the evolution of mixed traffic flow dynamics with the increase in CAV penetration rates within the mixed flow. Results show that the condition of traffic safety is greatly improved with the increase in the CAV penetration rate. More cautious car-following strategy of the CAV would contribute to a greater benefit on traffic safety, though less gain in capacity. With the increase in CAV penetration rate, the portion of smooth driving is increased. Velocity difference between vehicles is decreased and traffic flow is greatly smoothed. Stop-and-go traffic will be greatly eased.
Recent technological advancements bring the Connected and Autonomous Vehicles (CAVs) era closer to reality. CAVs have the potential to vastly improve road safety by taking the human driver out of the driving task. However, the evaluation of their safety impacts has been a major challenge due to the lack of real-world CAV exposure data. Studies that attempt to simulate CAVs by using either a single or integrating multiple simulation platforms have limitations, and in most cases, consider a small element of a network (e.g. a junction) and do not perform safety evaluations due to inherent complexity. This paper addresses this problem by developing a decision making CAV control algorithm in the simulation software VISSIM, using its External Driver Model Application Programming Interface. More specifically, the developed CAV control algorithm allows a CAV, for the first time, to have longitudinal control, search adjacent vehicles, identify nearby CAVs and make lateral decisions based on a ruleset associated with motorway traffic operations. A motorway corridor within M1 in England is designed in VISSIM and employed to implement the CAV control algorithm. Five simulation models are created, one for each weekday. The baseline models (i.e. CAV market penetration: 0%) are calibrated and validated using real-world minute-level inductive loop detector data and also data collected from a radar-equipped vehicle. The safety evaluation of the proposed algorithm is conducted using the Surrogate Safety Assessment Model (SSAM). The results show that CAVs bring about compelling benefit to road safety as traffic conflicts significantly reduce even at relatively low market penetration rates. Specifically, estimated traffic conflicts were reduced by 12-47%, 50-80%, 82-92% and 90-94% for 25%, 50%, 75% and 100% CAV penetration rates respectively. Finally, the results indicate that the presence of CAVs ensured efficient traffic flow.
Research related to autonomous vehicles and to their implications for human-machine interactions is on the rise. Advanced Driver Assistance Systems have become increasingly popular in vehicles currently deployed on the market, making researchers wonder about potential risks in case of technology failures for drivers that become accustomed to the use of such technology. To further our understanding of such concern, this work looks at the currently available data from autonomous vehicles field testing that has been carried out in California from 2014 to 2017. Our examination includes both qualitative and quantitative analyses, respectively, based on (i) the type of response in terms of control takeover in off-nominal scenarios that led to collisions involving autonomous vehicles; and (ii) the time to takeover after disengagements of the autonomous technology that acts as “brain” of the vehicle, with the request to the human driver to regain control of the vehicle. Our findings include expected values for the response time, discussion of factors that affect dispersion, presentation of how to determine trust and experience effects in the data, as well as a careful comparison with state-of-the-art literature on the topic.
Safety evaluation based on historical crashes usually has a lot of limitations. In previous studies, near-crashes are considered as surrogate data for safety evaluation. One challenge for the use of near-crashes data is the difficulty of data collection. The driving simulators and naturalistic driving data may not be suitable for safety evaluation at specific sites. The observational site-based methods such as human observers and video analysis also suffer from some limitations such as long time data processing or reduced performance influenced by weather or light condition. The roadside Light Detection and Ranging (LiDAR)-enhanced infrastructure provides a new solution for real-time data collection without the impact from weather or light. The high-resolution trajectories of all road users can be obtained from roadside LiDAR data. This paper aims to fill these gaps by presenting a method for near-crash identification based on the trajectories of road users extracted from roadside LiDAR data. This paper focused on vehicle-pedestrian near-crash identification particularly considering the increased risk of vehicle-pedestrian conflicts. Three parameters: Time Difference to the Point of Intersection (TDPI); Distance between Stop Position and Pedestrian (DSPP); Vehicle-pedestrian speed-distance profile, were developed for vehicle-pedestrian near-crash identification. The authors also recommended the thresholds for risk assessment of pedestrian safety. This method was coded into an automatic procedure for near-crash identification. This method is expected to significantly improve the current evaluation of pedestrian safety.
In general, there are two kinds of cooperative driving strategies, planning based strategy and ad hoc negotiation based strategy, for connected and automated vehicles (CAVs) merging problems. The planning based strategy aims to find the global optimal passing order, but it is time-consuming when the number of considered vehicles is large. In contrast, the ad hoc negotiation based strategy runs fast, but it always finds a local optimal solution. In this paper, we propose a grouping based cooperative driving strategy to make a good tradeoff between time consumption and coordination performance. The key idea is to fix the passing orders for some vehicles whose inter-vehicle headways are small enough (e.g., smaller than the pre-selected grouping threshold). From the viewpoint of optimization, this method reduces the size of the solution space. A brief analysis shows that the sub-optimal passing order found by the grouping based strategy has a high probability to be close to the global optimal passing order, if the grouping threshold is appropriately chosen. A series of simulation experiments are carried out to validate that the proposed strategy can yield a satisfied coordination performance with less time consumption and is promising to be used in practice.
In the next decades, road transport will undergo a deep transformation with the advent of connected and automated vehicles (CAVs), which promise to drastically change the way we commute. CAVs hold significant potential to positively affect traffic flows, air pollution, energy use, productivity, comfort, and mobility. On the other hand, there is an increasing number of sources and reports highlighting potential problems that may arise with CAVs, such as, conservative driving (relaxed thresholds), problematic interaction with human-driven vehicles (inability to take decisions based on eye contact or body language) and increased traffic demand. Therefore, it is of high importance to assess vehicle automated functionalities in a case-study simulation. The scope of this paper is to present some preliminary results regarding the impact assessment of cooperative adaptive cruise control (CACC) on the case-study of the ring road of Antwerp, which is responsible for almost 50% of the traffic and pollution of the city. Scenarios with various penetration rates and traffic demands were simulated showing that coordination of vehicles may be needed to significantly reduce traffic congestion and energy use.
The objective of this study was to develop a heterogeneous traffic-flow model to study the possible impact of connected and autonomous vehicles (CAVs) on the traffic flow. Based on a recently proposed two-state safe-speed model (TSM), a two-lane cellular automaton (CA) model was developed, wherein both the CAVs and conventional vehicles were incorporated in the heterogeneous traffic flow. In particular, operation rules for CAVs are established considering the new characteristics of this emerging technology, including autonomous driving through the adaptive cruise control and inter-vehicle connection via short-range communication. Simulations were conducted under various CAV-penetration rates in the heterogeneous flow. The impact of CAVs on the road capacity was numerically investigated. The simulation results indicate that the road capacity increases with an increase in the CAV-penetration rate within the heterogeneous flow. Up to a CAV-penetration rate of 30%, the road capacity increases gradually; the effect of the difference in the CAV capability on the growth rate is insignificant. When the CAV-penetration rate exceeds 30%, the growth rate is largely decided by the capability of the CAV. The greater the capability, the higher the road-capacity growth rate. The relationship between the CAV-penetration rate and the road capacity is numerically analyzed, providing some insights into the possible impact of the CAVs on traffic systems.
Adaptive cruise control (ACC) has been considered one of the critical components of automated driving. ACC adjusts vehicle speeds automatically by measuring the status of the ego-vehicle and leading vehicle. Current commercial ACCs are designed to be comfortable and convenient driving systems. Little attention is paid to the safety impacts of ACC, especially in traffic oscillations when crash risks are the highest. The primary objective of this study was to evaluate the impacts of ACC parameter settings on rear-end collisions on freeways. First, the occurrence of a rear-end collision in a stop-and-go wave was analyzed. A car-following model in an integrated ACC was developed for a simulation analysis. The time-to-collision based factors were calculated as surrogate safety measures of the collision risk. We also evaluated different market penetration rates considering that the application of ACC will be a gradual process. The results showed that the safety impacts of ACC were largely affected by the parameters. Smaller time delays and larger time gaps improved safety performance, but inappropriate parameter settings increased the collision risks and caused traffic disturbances. A higher reduction of the collision risk was achieved as the ACC vehicle penetration rate increased, especially in the initial stage with penetration rates of less than 30%. This study also showed that in the initial stage, the combination of ACC and a variable speed limit achieved better safety improvements on congested freeways than each single technique.
This paper develops a control approach with correctness guarantees for the simultaneous operation of lane keeping and adaptive cruise control. The safety specifications for these driver assistance modules are expressed in terms of set invariance. Control barrier functions are used to design a family of control solutions that guarantee the forward invariance of a set, which implies satisfaction of the safety specifications. The control barrier functions are synthesized through a combination of sum-of-squares program and physics-based modeling and optimization. A real-time quadratic program is posed to combine the control barrier functions with performance-based control Lyapunov functions, such that the generated feedback control guarantees the safety of the composed driver assistance modules in a formally correct manner. Importantly, the quadratic program admits a closed-form solution that can be easily implemented.
Brian Hayes discusses the possibilities of future cars being driven by computers and their impact on the lives of the people. Brian feels that if millions of them ever roam the public highways, they will be far safer than cars driven by people. If accidents become rare enough, one would expect to see changes in attitudes and behavior, and perhaps in the design of vehicles. A 99-percent improvement in safety will still leave tens of thousands of auto accidents every year, which may require new legal and financial mechanisms for compensating victims. Most collisions today are attributed to driver error rather than a defect or malfunction in the vehicle. Almost 90 percent of American workers commute by car, most of them alone, with a median trip duration of just under half an hour each way.
Despite enhanced safety strategies, in-vehicles technologies, and improvements in infrastructure, urban transportation networks are still accident-prone. Connected vehicle offers the possibility to exchange data with vehicles and infrastructure in an effort to improve safety. The main objective of the research reported in this paper is to evaluate the potential safety benefits of deploying a connected vehicle system on a traffic network in the presence of a work zone. The modeled connected vehicle system in the research reported in this paper uses vehicle-to-vehicle (VTV) communication to share information about work zone links and link travel times. Vehicles which receive work zone information will also modify their driving behavior by increasing awareness and decreasing aggressiveness. This paper also proposes a decaying average travel time dynamic route guidance algorithm which exhibits weighted information decay. Traffic microsimulation software is used to model the network and a C plugin is developed to implement connected vehicle in the simulation. The surrogate safety measure improved time to collision (TTC) is used to assess the safety of the network. Various market penetrations of connected vehicles were utilized along with three different behavior models to account for the uncertainty in driver response to connected vehicle information. The results show that network safety is strongly correlated with the behavior model used; conservative models yield conservative changes in network safety. The results also show that market penetrations of connected vehicles under 40% contribute to a safer traffic network, while market penetrations above 40% decrease network safety. The findings of the research reported in this paper indicate connected vehicle technology can have unintended consequences, as seen in decreased safety at high market penetrations, requiring researchers to develop additional applications to mitigate these effects.
The IEEE 802.11 working group proposed a standard for the physical and medium access control layers of vehicular networks called 802.11p. In this paper we report experimental results obtained from communication between vehicles using 802.11p in a real scenario. The main motivation is the lack of studies in the literature with performance data obtained from off-the-shelf 801.11p devices. Our study characterizes the typical conditions of an 802.11p point-to-point communication. Such a study serves as a reference for more refined simulation models or to motivate enhancements in the PHY/MAC layers. Field tests were carried out varying the vehicle's speed between 20 and 60 km/h and the packet length between 150 and 1460 bytes, in order to characterize the range, throughput, latency, jitter and packet delivery rates of 802.11p links. It was observed that communication with vehicles in motion is unstable sometimes. However, it was possible to transfer data at distances over 300 m, with data rates sometimes exceeding 8 Mbit/s.
The ability to predict the response of a vehicle in a stream of traffic to the behaviour of its predecessor is important in estimating what effect changes to the driving environment will have on traffic flow. Various proposed to explain this behaviour have different strengths and weaknesses. The paper constructs a new model for the response of the following vehicle based on the assumption that each driver sets limits to his desired braking and acceleration rates. The parameters in the model correspond directly to obvious characteristics of driver behaviour and the paper goes on to show that when realistic values are assigned to the parameters in a simulation, the model reproduces the characteristics of real traffic flow.
Current research for vehicular communication is largely driven by the allocation of 75MHz spectrum in the 5.9GHz band for dedicate short range communications (DSRC) in North America. The IEEE 802.11p physical (PHY) layer and medium access control (MAC) layer that is currently under standardization aim at communication distances of up to 1000m. In this paper we evaluate the maximum communication distance of an IEEE 802.11p vehicular ad-hoc network including mobility effects and multi-path propagation. Furthermore the communication distance for different path loss exponents is evaluated.
A traffic encounter between individual road users is a process of continuous interplay over time and space and may be seen as an elementary event with the potential to develop into an accident. This paper proposes a framework for organising all traffic encounters into a severity hierarchy based on some operational severity measure. A severity hierarchy provides a description of the safety situation and trade-off between safety and efficiency in the traffic system. As a first approach to study the encounter process, a set of indicators is proposed to describe an encounter. These indicators allow for a continuous description even if the relationship between the road users changes during the process (e.g., when they are on a collision course or leave it). Automated video analysis is suggested as a tool that will allow data collection for validation of the proposed theories.
A structure is proposed to connect the decisions which a driver has to make before changing lanes. The model is intended to cover the urban driving situation, where traffic signals, obstructions and heavy vehicles all exert an influence. The structure is designed to ensure that the vehicles in traffic simulations behave logically when confronted with situations commonly encountered in real traffic. The specific mathematical expression of the questions embedded in the decision process and employed in the present implementation of the model are not critical and can be replaced by alternatives, but the heirarchy of the decisions is crucial. On the basis of experience to date, the lane changing model produces a realistic simulation of driver behaviour and has proved very robust under a wide range of conditions.
LEVITATE: Road safety impacts of connected and automated vehicles
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Critical reasons for crashes investigated in the national motor vehicle crash causation survey
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Surrogate safety assessment model and validation: Final report
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Detailed list of sub-use cases, applicable forecasting methodologies and necessary output variables
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