Figure 3 - uploaded by Sumbal Malik
Content may be subject to copyright.
Source publication
In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has th...
Context in source publication
Similar publications
Self-driving vehicles must be controlled by navigation algorithms that ensure safe driving for passengers, pedestrians and other vehicle drivers. One of the key factors to achieve this goal is the availability of effective multi-object detection and tracking algorithms, which allow to estimate position, orientation and speed of pedestrians and othe...
Accurate prediction of vehicle trajectories is essential for safe and efficient navigation in urban environments, particularly with the increasing prevalence of autonomous vehicles and intelligent transportation systems. This paper introduces a deep learning-based approach for predicting vehicle trajectories on urban roads in real time. The method...
Global navigation satellite systems (GNSSs) are commonly used to measure the position and time globally. A GNSS is convenient owing to its ability to measure accurate position relatively without using assistive tools for navigation by comparing with other sensors. Based on these benefits, the applicable area is expanding to commercial and social us...
The autonomous vehicle is an innovative field for the application of machine learning algorithms. Controlling an agent designed to drive safely in traffic is very complex as human behavior is difficult to predict. An individual’s actions depend on a large number of factors that cannot be acquired directly by visualization. The size of the vehicle,...
In Level-3 autonomous driving, drivers are required to take over in an emergency upon receiving a request from an autonomous vehicle (AV). However, before the deadline for the takeover request expires, drivers are not considered fully responsible for the accident, which may make them hesitant to assume control and take on full liability before the...
Citations
... This includes, for example, adverse weather conditions (e.g., wind, rain, or fog), diverse terrains (e.g., hilly, flat, urban, and open fields), and varying mission profiles (e.g., longrange, short-range, surveillance, and tracking) [16], [28], [45]. Despite the availability of various sUAS simulation tools and software applications [19], [52], simulation testing remains predominantly a manual, or at best partially-automated process. As a result, current sUAS simulation testing practices result in three main pain points for sUAS application developers. ...
... This blueprint can be utilized by the M-Agent to produce a list of destinations, specifying the locations the car must visit during simulation. For widely used simulation tools such as CARLA [52], the Env-Agent can automatically generate the necessary configuration files (e.g., CarlaSettings.ini) to initialize the CARLA simulation environment [52] with rainy conditions. ultimately, the generated simulation logs can be analyzed by the Analytics-Agent, leveraging its comprehensive domain knowledge of analyzing parameters of autopilots of autonomous cars. ...
... This blueprint can be utilized by the M-Agent to produce a list of destinations, specifying the locations the car must visit during simulation. For widely used simulation tools such as CARLA [52], the Env-Agent can automatically generate the necessary configuration files (e.g., CarlaSettings.ini) to initialize the CARLA simulation environment [52] with rainy conditions. ultimately, the generated simulation logs can be analyzed by the Analytics-Agent, leveraging its comprehensive domain knowledge of analyzing parameters of autopilots of autonomous cars. ...
Thorough simulation testing is crucial for validating the correct behavior of small Uncrewed Aerial Systems (sUAS) across multiple scenarios, including adverse weather conditions (such as wind, and fog), diverse settings (hilly terrain, or urban areas), and varying mission profiles (surveillance, tracking). While various sUAS simulation tools exist to support developers, the entire process of creating, executing, and analyzing simulation tests remains a largely manual and cumbersome task. Developers must identify test scenarios, set up the simulation environment, integrate the System under Test (SuT) with simulation tools, formulate mission plans, and collect and analyze results. These labor-intensive tasks limit the ability of developers to conduct exhaustive testing across a wide range of scenarios. To alleviate this problem, in this paper, we propose AutoSimTest, a Large Language Model (LLM)-driven framework, where multiple LLM agents collaborate to support the sUAS simulation testing process. This includes: (1) creating test scenarios that subject the SuT to unique environmental contexts; (2) preparing the simulation environment as per the test scenario; (3) generating diverse sUAS missions for the SuT to execute; and (4) analyzing simulation results and providing an interactive analytics interface. Further, the design of the framework is flexible for creating and testing scenarios for a variety of sUAS use cases, simulation tools, and SuT input requirements. We evaluated our approach by (a) conducting simulation testing of PX4 and ArduPilot flight-controller-based SuTs, (b) analyzing the performance of each agent, and (c) gathering feedback from sUAS developers. Our findings indicate that AutoSimTest significantly improves the efficiency and scope of the sUAS testing process, allowing for more comprehensive and varied scenario evaluations while reducing the manual effort.
... Each of these simulators provides unique features and capabilities, contributing to the advancement of research and development in advanced driving systems. An interesting set of very recent surveys involving a comprehensive list of the main simulation platforms and their comparison is available in [21], [22], [23], offering a valuable resource for researchers and practitioners alike. ...
... Based on our research, we provide our own definition of a complex environment and present a high-level abstract (Figure 1) that encapsulates the most relevant dynamics representative of a complex urban environment. To facilitate understanding and highlight challenges in specific urban settings, we break down the environment into various scenes such as fewer dynamics, roundabout challenges, sharp turns, etc., as depicted in Figure 1 (readers are highly encouraged to refer to the authors' recent publication [2] for further details). To address the challenges posed by the urban environment, the traditional autonomous driving systems at L2 and L3 are striving towards achieving L5, which represents fully autonomous driving. ...
A specialized version of collaborative driving is convoy driving. It is referred to as the practice of driving more than one vehicle consecutively in the same lane with a small inter-vehicle distance, maintaining the same speed. Extensive research has been conducted on convoys of heavy-duty trucks on the highway; however, limited research has studied convoy driving in an urban environment. The complex dynamics of an urban environment require short-lived collaboration with varying numbers of vehicles rather than collaborating over hours. The motivation of this research is to investigate how convoy driving can be realized to address the challenges of an urban environment and achieve the benefits of autonomous driving such as reduced fuel consumption, travel time, improved safety, and ride comfort. In this work, the best-fitted coalitional game framework is utilized to formulate the convoy driving problem as a coalition formation game in an urban environment. A hypothesis is formulated that traveling in a coalition is more beneficial for a vehicle than traveling alone. In connection with this, a coalitional game and an all-comprehensive utility function are designed, modeled, and implemented to facilitate the formation of autonomous vehicle coalitions for convoy driving. Multiple solution concepts, such as the Shapley allocation, the Nucleolus, and the Core, are implemented to solve and analyze the proposed convoy driving game. Furthermore, several coalition formation strategies such as traveling mode selection, selecting optimal coalitions, and making decisions about coalition merging are developed to analyze the behavior of the vehicles. In addition to this, extensive numerical experiments with different settings are conducted to evaluate and validate the performance of the proposed study. The experimental results proved the hypothesis that traveling in a convoy is significantly more beneficial than traveling alone. We conclude that traveling in a convoy is beneficial for coalition sizes of two to four vehicles with an inter-vehicle spacing of less than 4 m considering the limitations of an urban environment. Traveling in a coalition allows vehicles to save on fuel, minimize travel time and enhance safety and comfort. Furthermore, the findings of this research state that achieving the enormous benefits of traveling in a coalition requires finding the right balance between inter-vehicle distance and coalition size. In the future, we plan to extend this work by studying the evolving dynamics of the coalitions and the environment.
Roadside LiDAR is a way to enhance the perception capabilities of connected autonomous vehicles (CAVs) using the vehicle-to-infrastructure (V2I) cooperation system by providing complementary and beyond visual range information. However, occlusion remains challenging in roadside LiDAR required to be considered in deployment. Therefore, this paper proposes a roadside LiDAR deployment optimization framework that considers the spatial-temporal occlusion arising from vehicle on urban streets. First, a pose deployment optimization model was constructed to maximize the utilization of the laser beams and minimize the spatial-temporal occlusion. And a network deployment problem was formulated considering the coverage intensity and cost performance with the pose deployment constraints. Then, a stagewise greedy algorithm combined with particle swarm optimization algorithm (GA-PSO) was proposed and a elitist preservation genetic algorithm (EGA) was improved to optimize the roadside LiDAR layout and pose in traffic network. Experiment results showed that the detection accuracy of proposed method can reach to 90.6%, outperforming current methods in different traffic conditions. Furthermore, the proposed method enables more complete target point cloud outlines capturing under various traffic and occlusion conditions.
Recently, the development of autonomous vehicles (AVs) and intelligent driver assistance systems has drawn significant attention from the public. Despite these advancements, AVs may encounter critical situations in real-world scenarios that can lead to severe traffic accidents. This review paper investigated these critical scenarios, categorizing them under weather conditions, environmental factors, and infrastructure challenges. Factors such as attenuation and scattering severely influence the performance of sensors and AVs, which can be affected by rain, snow, fog, and sandstorms. GPS and sensor signals can be disturbed in urban canyons and forested regions, which pose vehicle localization and navigation problems. Both roadway infrastructure issues, like inadequate signage and poor road conditions, are major challenges to AV sensors and navigation systems. This paper presented a survey of existing technologies and methods that can be used to overcome these challenges, evaluating their effectiveness, and reviewing current research to improve AVs’ robustness and dependability under such critical situations. This systematic review compares the current state of sensor technologies, fusion techniques, and adaptive algorithms to highlight advances and identify continuing challenges for the field. The method involved categorizing sensor robustness, infrastructure adaptation, and algorithmic improvement progress. The results show promise for advancements in dynamic infrastructure and V2I systems but pose challenges to overcoming sensor failures in extreme weather and on non-maintained roads. Such results highlight the need for interdisciplinary collaboration and real-world validation. Moreover, the review presents future research lines to improve how AVs overcome environmental and infrastructural adversities. This review concludes with actionable recommendations for upgrading physical and digital infrastructures, adaptive sensors, and algorithmic upgrades. Such research is important for AV technology to remain in the zone of advancement and stability.
With the rapid development of autonomous driving technology, ensuring the safety and reliability of vehicles under various complex and adverse conditions has become increasingly important. Although autonomous driving algorithms perform well in regular driving scenarios, they still face significant challenges when dealing with adverse weather conditions, unpredictable traffic rule violations (such as jaywalking and aggressive lane changes), inadequate blind spot monitoring, and emergency handling. This review aims to comprehensively analyze these critical issues, systematically review current research progress and solutions, and propose further optimization suggestions. By deeply analyzing the logic of autonomous driving algorithms in these complex situations, we hope to provide strong support for enhancing the safety and reliability of autonomous driving technology. Additionally, we will comprehensively analyze the limitations of existing driving technologies and compare Advanced Driver Assistance Systems (ADASs) with Full Self-Driving (FSD) to gain a thorough understanding of the current state and future development directions of autonomous driving technology.
With the advent of autonomous vehicles, sensors and algorithm testing have become crucial parts of the autonomous vehicle development cycle. Having access to real-world sensors and vehicles is a dream for researchers and small-scale original equipment manufacturers (OEMs) due to the software and hardware development life-cycle duration and high costs. Therefore, simulator-based virtual testing has gained traction over the years as the preferred testing method due to its low cost, efficiency, and effectiveness in executing a wide range of testing scenarios. Companies like ANSYS and NVIDIA have come up with robust simulators, and open-source simulators such as CARLA have also populated the market. However, there is a lack of lightweight and simple simulators catering to specific test cases. In this paper, we introduce the SLAV-Sim, a lightweight simulator that specifically trains the behaviour of a self-learning autonomous vehicle. This simulator has been created using the Unity engine and provides an end-to-end virtual testing framework for different reinforcement learning (RL) algorithms in a variety of scenarios using camera sensors and raycasts.
