Autonomous vehicles must operate safely in their dynamic and continuously-changing environment. However, the operating environment of an autonomous vehicle is complicated and full of various types of uncertainties. Additionally, the operating environment has many configurations, including static and dynamic obstacles with which an autonomous vehicle must avoid collisions. Through various approaches targeting on environment configuration for autonomous vehicles have shown promising results, their effectiveness in dealing with a continuous-changing environment is limited. Thus, it is essential to learn realistic environment configurations ofcontinuously-changing environment, under which an autonomous vehicle should be tested regarding its ability to avoid collisions. Featured with agents dynamically interacting with the environment, Reinforcement Learning (RL) has shown great potential in dealing with complicated problems requiring adapting to the environment. To this end, we present an RL-based environment configuration learning approach, i.e., DeepCollision, which intelligently learns environment configurations that lead an autonomous vehicle to crash.DeepCollision employs Deep Q-Learning as the RL solution, and selects collision probability, as the safety measure, to construct the reward function. We trained four DeepCollision models and conducted an experiment to compare them with two baselines, i.e., random and greedy. Results show that DeepCollision demonstrated significantly better effectiveness in generating collisions compared with the baselines. We also provide recommendations on configuring DeepCollision with the most suitable time interval based on different road structures.

Cyber-physical systems (CPSs) have emerged as a potential enabling technology to handle the challenges in social and economic sustainable development. Sinc

Autonomous driving systems (ADSs) must be sufficiently tested to ensure their safety. Though various ADS testing methods have shown promising results, they are limited to a fixed vehicle characteristics setting (VCS). The impact of variations in vehicle characteristics (e.g., mass, tire friction) on the safety of ADSs has not been sufficiently and systematically studied. Such variations are often due to wear and tear, production errors and so on, which may lead to unexpected driving behaviours of ADSs. To this end, in this article, we propose a method, named SafeVar , to systematically find minimum variations to the original vehicle characteristics setting, which affect the safety of the ADS deployed on the vehicle. To evaluate the effectiveness of SafeVar , we employed two ADSs and conducted experiments with two driving scenarios. Results show that SafeVar , equipped with NSGA-II, generates more critical settings that put the vehicle into unsafe situations, as compared with the baseline algorithm. We also identified critical vehicle characteristics and reported to which extent varying their settings put the ADS vehicle into unsafe situations.