Download or read book Criticality Assessment of Simulation Based AV/ADAS Test Scenarios written by Bo Shian Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With a fast-paced growth of Automated Driving Systems (ADV) and Advanced Driver Assistance Systems (ADAS), simulation-based validation and verification (V&V) has become an essential way to validate the reliability of the safety algorithms and components before performing the field tests. Virtual driving scenarios typically consist of trajectories of surrounding agents, road geometry, environmental effects, lighting conditions, etc. It is necessary to identify the specific region in the scenario parameter space, that makes the scenario 'critical' such that the ADS features could play an essential role to help the driver avoid accidents. The criticality of the scenario could depend on multiple parameters, such as ego vehicle speed, vehicle dynamics, and other actor’s trajectories. However, the definition of criticality should be independent of the ADS controller or driver models. In this thesis, we propose a novel approach to compute criticality using concepts from optimal control which does not require driver models or any specific controller. The key concept is that the value function obtained from the optimal control solution is an indicator of relative ease in the maneuver and the probability of a safe result. The uniqueness of this concept is that the value function is an outcome of optimal ADS control, and it incorporates crash probability and difficulty of maneuver. Moreover, this approach incorporates modeling uncertainty and stochasticity in perception and localization. In this thesis we demonstrate the approach using three optimal control algorithms namely, dynamic programming (DP), Markov Decision Process iii (MDP), and Reinforcement Learning (RL). This approach has three key phases- 1) develop logical scenarios under several highway situations based on the real crash data, 2) develop an optimal control-based strategy to generate safety-critical simulation scenarios for autonomous vehicle obstacle avoidance maneuvers, and 3) extend the approach further to incorporate modeling uncertainties and calculate the crash probability or the value function. To better demonstrate the proposed approach, an obstacle avoidance driving scenario has been used as an example in this thesis.