Virtual testing of automated vehicles using simulations is essential during their development. When it comes to the testing of motion planning algorithms, one is mainly interested in challenging, critical scenarios for which it is hard to find a feasible solution. However, these situations are rare under usual traffic conditions, demanding an automatic generation of critical test scenarios. We present an approach that automatically generates critical scenarios based on a minimization of the solution space of the vehicle under test. By formulating a scenario parametrization and automatic determination of relevant parameter intervals, we are able to optimize the criticality of complex scenarios. We use evolutionary algorithms to tackle the resulting highly nonlinear optimization problem. Compared to our previous approach, we are now able to handle complex situations, in particular those involving intersections. Finally, we demonstrate our approach by generating critical scenarios from initially uncritical scenarios.

Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms.

Automated Driving Systems (ADS) have made great achievements in recent years thanks to the efforts from both academia and industry. A typical ADS is composed of multiple modules, including sensing, perception, planning, and control, which brings together the latest advances in different domains. Despite these achievements, safety assurance of ADS is of great significance, since unsafe behavior of ADS can bring catastrophic consequences. Testing has been recognized as an important system validation approach that aims to expose unsafe system behavior; however, in the context of ADS, it is extremely challenging to devise effective testing techniques, due to the high complexity and multidisciplinarity of the systems. There has been great much literature that focuses on the testing of ADS, and a number of surveys have also emerged to summarize the technical advances. Most of the surveys focus on the system-level testing performed within software simulators, and they thereby ignore the distinct features of different modules. In this paper, we provide a comprehensive survey on the existing ADS testing literature, which takes into account both module-level and system-level testing. Specifically, we make the following contributions: (1) we survey the module-level testing techniques for ADS and highlight the technical differences affected by the features of different modules; (2) we also survey the system-level testing techniques, with focuses on the empirical studies that summarize the issues occurring in system development or deployment, the problems due to the collaborations between different modules, and the gap between ADS testing in simulators and the real world; (3) we identify the challenges and opportunities in ADS testing, which pave the path to the future research in this field.

https://dl.acm.org/doi/pdf/10.1145/3579642

A Survey on Automated Driving System Testing: Landscapes and Trends

Congestion is an ongoing problem for many urban centres worldwide (such as London), leading to excessive delays, noise and air pollution, frustrated drivers, and high energy consumption. The carbon footprint of conventional transport systems can be high as a result and transport is among the highest contributors of greenhouse gas emissions. Therefore, with the growing interest in developing connected fully autonomous vehicles (ConFAVs), there is a pressing need to consider their effects within the congested urban setting. To address this, the current research study was designed to investigate the potential for ConFAVs in providing a sustainable transport solution. During this research, a simulation model was developed, calibrated, and validated using field data collected from several sites in East London, using the graphical user interface (GUI) simulation software PTV VISSIM to simulate the proposed driving and car following behaviour, which included the platooning of these ConFAVs, to assess how they could improve the level of service of the roads. Using the new model, this research addresses the shortcomings of two other adaptations of the Wiedemann 99 car-following models by changing the ConFAV’s behaviour to be more cautious when travelling behind a human driven vehicle, and less cautious when behind another ConFAV. As little is known about the transitional period from zero autonomy to full autonomy on the already congested road network, due to the fact that these vehicles are typically tested in small numbers (often one at a time in a controlled environment), the present research study introduced ConFAVs to the simulated network gradually and in large numbers at 20% intervals (namely 0% where there are no ConFAVs, 20%, 40%, 60%, 80%, and finally 100% where all vehicles within the network were ConFAVs). The average delays and subsequent level of service for the roads within the networks were then assessed against each ConFAV penetration level. This helped understand how the network’s efficiency changes when the number of ConFAVs increases, and the potential benefits for these self-driving vehicles on congestion and the ensuing greenhouse gas emissions. The model showed that a reduction in delay of up to 100% can be achieved by introducing ConFAVs, which translates to a significant reduction in greenhouse gas emissions. This, coupled with the fact that ConFAVs are predominantly electric, points to a future sustainable road transport system. The primary purpose of this research would be to investigate the potential of ConFAVs in reducing traffic congestion and, as a result, greenhouse gas emissions.

/pdf/potential-of-connected-fully-autonomous-vehicles-in-reducing-2kva6nie.pdf

Potential of Connected Fully Autonomous Vehicles in Reducing Congestion and Associated Carbon Emissions

Among so many autonomous driving technologies, autonomous lane changing is an important application scenario, which has been gaining increasing amounts of attention from both industry and academic communities because it can effectively reduce traffic congestion and improve road safety. However, most of the existing researchers transform the multi-objective optimization problem of lane changing trajectory into a single objective problem, but how to determine the weight of the objective function is relatively fuzzy. Therefore, an optimization method based on the combination of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which provides a new idea for solving the multi-objective optimization problem of lane change trajectory algorithm, is proposed in this paper. Firstly, considering the constraints of lane changing and combining with the collision detection algorithm, the feasible lane changing trajectory cluster is obtained based on the quintic polynomial. In order to ensure the comfort, stability and high efficiency of the lane changing process, the NSGA-II Algorithm is used to optimize the longitudinal displacement and time of lane changing. The continuous ordered weighted averaging (COWA) operator is introduced to calculate the weights of three objective optimization functions. Finally, the TOPSIS Algorithm is applied to obtain the optimal lane change trajectory. The simulations are conducted, and the results demonstrate that the proposed method can generate a satisfactory trajectory for automatic lane changing actions.

/pdf/optimal-lane-change-path-planning-based-on-the-nsga-ii-and-258ern4b.pdf

Optimal Lane Change Path Planning Based on the NSGA-II and TOPSIS Algorithms

Navigation in close proximity with pedestrians is a challenge on the way to fully automated vehicles. Pedestrian-friendly navigation requires an understanding of pedestrian reaction and intention. Merely safety based reactive systems can lead to sub-optimal navigation solutions resulting in the freezing of the vehicle in many scenarios. Moreover, a strictly reactive method can produce unnatural driving patterns which cannot guarantee the legibility or social acceptance of the automated vehicle. This work presents a proactive maneuvering method adapted to navigation in close interaction with pedestrians using a dynamic channel approach. The method allows to proactively explore the navigation options based on anticipating pedestrians cooperation. The navigation is tested in frontal and lateral crossing scenarios with variable space density. The system is implemented under ROS, and compared with the probabilistic Risk-RRT planning method. The results are evaluated based on the safety and comfort of the pedestrians, and the quality of the vehicle's trajectory.

/pdf/proactive-and-smooth-maneuvering-for-navigation-around-1me1m4ne.pdf

Proactive And Smooth Maneuvering For Navigation Around Pedestrians

As the development of the autonomous vehicles, the test and evaluation of the autonomy of autonomous vehicles has receiving considerable attentions from both academic and industrial research fields recently. How to rapid identify the performance boundaries of autonomous vehicles and then to accelerate the testing has been considered as one of the most important problems. Simulation test and challenging scenarios are considered as two effective ways to solve above problems. In this research, a framework of rapid generation of challenging simulation scenarios based on adversarial test was proposed. An optimization model based on newly developed indicators, i.e., safety, smoothness, sharpness and smartness, was proposed to assess the autonomy of autonomous vehicles. Due to the high dimensionality of the configuration space and computation complexity of the optimization problem, a quantum genetic algorithm was employed to solve the optimization problem owing to its high search ability and lower population size required. A simulation system based on Prescan, Carsim and Simulink was built to validate the effectiveness of the proposed method. Comparative experiments also were conducted to illustrate the efficiency of the proposed optimization algorithm.

Rapid Generation of Challenging Simulation Scenarios for Autonomous Vehicles Based on Adversarial Test

This paper proposes a lane change path planning method for Autonomous Vehicles (AVs) driving in structured environments. Due to the complexity of dynamically changing structured environment, the path planner generally requires to generate a safe and smooth path in a short time when changing lanes. Therefore, we propose an efficient lane change path planning method, which can take into account both the curvature and heading angle of the planned path, ensuring the safety and stability of the lane change maneuver. It builds upon existing method using quintic spline with second-order geometric (G2-) continuity in a Frenet coordinate system. By calculating the optimal prediction time interval of the lane change maneuver, the heading angle was kept within a safe range. In order to improve the real-time performance, we use road prior information to optimize the selection of sampling points, effectively reducing the number of candidate paths. In addition, beyond the sampling points, the problem of continuous lane change is solved by adding mid-adjust point and target point, so that AVs have the ability to complete complex movements. In an urban environment, the proposed method is tested on our experimental autonomous vehicle equipped with a modular ROS architecture. Experimental results show that the proposed algorithm can generate complex motion paths efficiently, and the curvature and heading angle of the planned path are continuous, which effectively improves the efficiency and stability of path planning.

Efficent Lane Change Path Planning based on Quintic spline for Autonomous Vehicles

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Rapid Generation of Challenging Simulation Scenarios for Autonomous Vehicles Based on Adversarial Test

Efficent Lane Change Path Planning based on Quintic spline for Autonomous Vehicles