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Aggregated results across all user trials tests for TTC and AA differentiated by C-ITS safety applications and type of vehicle (i.e., equipped non-equipped).
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Cooperative intelligent transport systems (C-ITS) are expected to considerably influence road safety, traffic efficiency and comfort. Nevertheless, their market penetration is still limited, on the one hand due to the high costs of installation and maintenance of the infrastructures and, on the other hand, due to the price of support automated driv...
Citations
... It is built upon the concept of motion primitives and action selection [35]. Motion primitives are obtained from the solution of an Optimal Control Problem (OCP) that minimises the longitudinal jerk, which is known to model human-like manoeuvres [35,36] and also used to predict driver intention [37]. Their solution yields longitudinal trajectories for space, velocity, acceleration, and jerk. ...
Automated Driving (AD) has been receiving considerable attention from industry, the public, and researchers for its ability to reduce accidents, emissions, and congestion. The purpose of this study is to extend the standardized Local Dynamic Map (LDM) by adding two new layers, and develop efficient and accurate algorithms designed to enhance AD by exploiting the LDM coupled with Cooperative Perception (CP). The LDM is implemented as a Neo4j graph database and extends the standard four-layer structure by adding a detection layer and a prediction layer. A custom Application Programming Interface (API) manages all incoming data, generates the LDM, and runs the algorithms. Currently, the API can match detected entities coming from different sources, correctly position them on the map even in the presence of high uncertainties in the data, and predict their future actions. We tested the developed LDM with real-world data, which we collected using a prototype vehicle from Centro Ricerche FIAT (CRF) Trento Branch—the supporting research center for this work—in urban, suburban, and highway areas of Trento, Italy. The results show that the developed solution is capable of accurately matching and predicting detected entities, is robust to high uncertainties in the data, and is efficient, achieving real-time performance in all scenarios. From these results we can conclude that the LDM and CP have the potential to be core parts of AD, bringing improvements to the development process.
... While CAVs can remarkably improve highway mobility and safety [7], CAV performance can be influenced by a number of factors such as roadworks, road surface conditions, merging and diverging sections, which can result in disengagements [8], [9]. Roadworks are common along highways, present due to improvement and maintenance activities [7]. ...
... Therefore, this paper focuses on the CAV operations in a highway during roadworks. In roadworks, the road layout is altered and vehicles have to adapt their usual trajectories to travel reliably within the new road configuration [9]. However, CAVs may fail to navigate safely and experience difficulties because the base map available in their path planning module does not reflect the altered road layout [10]. ...
Navigating through roadworks represents one of the main sources of safety risk for Connected and Autonomous Vehicles (CAVs) due to the altered road layouts. The built-in base maps do not normally reflect these changes, causing CAVs to experience difficulties in sensing and trajectory generation. Therefore, the objective of this paper is to evaluate different collision-free trajectory generation for CAVs at roadworks to improve safety and traffic performance. Trajectory generation algorithms using lane-level dynamic maps were examined for: 1) CAVs rely on data from in-vehicle sensor only; and 2) CAVs receive additional information via a Smart Traffic Cone (STC) in advance regarding roadwork configurations. Experiments were conducted at a controlled motorway facility operated by National Highways (England) using a vehicle instrumented with a suite of sensors. Schematics of the roadworks scenario were translated into an integrated simulation platform consisting of a traffic microsimulation (VISSIM) to simulate traffic dynamics and a sub-microscopic simulator (PreScan) capable of simulating vehicle autonomy and connectivity. Results indicate that traffic conflicts and delays decrease by 40% and 3% respectively when CAVs receive additional information in advance (i.e., Scenario 2) compared to the other scenario. These findings would assist road network operators in developing ‘CAV-enabled roadworks’ and vehicle manufacturers in designing a vehicle-based ‘roadworks assist’ system.
... Shifting intelligence from vehicles to road infrastructure, an innovative C-ITS solution was introduced in [9]. The purpose was to improve road safety via the integration of Another study [21] used different methodologies for vehicle on-board energy harvesting from shock absorbers. ...
(1) Background: The present development of transport networks focusses on the better management of fuels and energy and the preservation of the environment. To fulfill these desiderates, some countries have already reconsidered the deployment plans of new highways. This research studies the feasibility of less polluting, quasi-self-powered, intelligent highway infrastructure functional blocks accommodating functions for the future introduction of smart wireless sensor grids and connected autonomous vehicles. Subject of investigation are the possibilities of energy harvesting, and the intelligent management of resources. (2) Methods: the research investigates the main technologies for energy harvesting and recommends an optimal solution. It also proposes a framework for the intelligent, AI-based management of energy and the use of an optimized backup solution relying on 5G beamforming for energy supply of the local wireless sensing network devices; (3) Results: recommendations are made for the best energy harvesting solution, an architecture of the energy management system, an algorithm for energy management and backup solution based on 5G beamforming; (4) Conclusions: the research emphasizes the advantages and drawbacks for different solutions regarding energy harvesting in an intelligent green highway scenario with a focus on the infrastructure developed to accommodate future connected and autonomous vehicles. The term “intelligent highway” must be understood in the automotive industry to describe a network of roads where cars communicate with the infrastructure and among themselves for the purpose of avoiding congestion and performing the seamless operation of services, and a space where cars and infrastructure cooperatively process information for obtaining better road safety, less pollution, and efficient energy management. With the recent recession of conventional fuel availability and the increase in prices, a solution to improving autonomy of both cars and infrastructure might be welcomed.
... The Co-Driver paradigm is used in different contexts. Similarly to [58], in [61] a Co-Driver instance uses the vehicle state to predict dangerous behaviours and issue warnings, by means of V2V communication and sensors on the road. In [57] a Co-driver instance mimics the stop maneuvers of human drivers by concatenating different motion primitives to achieve more sophisticated maneuvers. ...
In this manuscript we address the problem of online optimal control for torque splitting in hybrid electric vehicles that minimises fuel consumption and preserves battery life. We divide the problem into the prediction of the future velocity profile (i.e. driver intention estimation) and the online optimal control of the hybrid powertrain following a Model Predictive Control (MPC) scheme. The velocity prediction is based on a bio-inspired driver model, which is compared on various datasets with two alternative prediction algorithms adopted in the literature. The online optimal control problem addresses both the fuel consumption and the preservation of the battery life using an equivalent cost given the estimated speed profile (i.e. guaranteeing the desired performance). The battery degradation is evaluated by means of a state-of-the-art electrochemical model. Both the predictor and the Energy Management System (EMS) are evaluated in simulation using real driving data divided into 30 driving cycles from 10 drivers characterised by different driving styles. A comparison of the EMS performances is carried out on two different benchmarks based on an offline optimization, in one case on the entire dataset length and in the second on an ideal prediction using two different receding horizon lengths. The proposed online system, composed by the velocity prediction algorithm and the optimal control MPC scheme, shows comparable performances with the previous ideal benchmarks in terms of fuel consumption and battery life preservation. The simulations show that the online approach is able to significantly reduce the capacity loss of the battery, while preserving the fuel saving performances.
IEEE 802.11p is an established standard for Wireless Access in Vehicular Environment (WAVE) to support Intelligent Transportation System (ITS), which is one of the most imperative Vehicular Ad hoc Network (VANET) applications. VANET Vehicle-to-Everything (V2X) communications require On-Board Unit (OBU) and On-Board Diagnostic (OBD) systems to be installed in vehicles. Such systems enhance driver safety by generating in-vehicle alerts but require advanced transportation infrastructure and are high-priced. Cost-effective smartphone-based V2X communication systems have been developed for improving driver and pedestrian safety by generating collision forewarning, traffic congestion alert, road anomalies alert, etc., to prevent road crashes. In this paper, we propose to further reduce the number of accidents by augmenting driver’s awareness about driving behaviors of neighboring drivers and forthcoming unfavorable road conditions. The system creates a vehicular network based on smartphones using Wi-Fi and detects driver behavior and road condition. Novelty of the proposed system lies in disseminating road status and driver behavior (detected considering complete context) to alert the driver in advance using smartphone Wi-Fi instead of special ITS communication infrastructure. Furthermore, an algorithm to ignore invalid beacons that do not contribute to situational awareness has been proposed. Implementation and testing of the system has been done in real time using two vehicles. For large-scale implementation, SUMO and NS-3 simulations have been used. The results indicate the efficacy of the proposed system with successful message dissemination up to 130 m. The proposed system aims at providing the flavor of ITS to developing countries in a cost-effective manner using a mobile smartphone.
