Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles

Testing and evaluation is a crucial step in the development and deployment of Connected and Automated Vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is of necessity to test the CAVs in safety-critical scenarios, which rarely happen in naturalistic driving environment. Therefore, how to purposely and systematically generate these corner cases becomes an important problem. Most existing studies focus on generating adversarial examples for perception systems of CAVs, whereas limited efforts have been put on the decision-making systems, which is the highlight of this paper. As the CAVs need to interact with numerous background vehicles (BVs) for a long duration, variables that define the corner cases are usually high dimensional, which makes the generation a challenging problem. In this paper, a unified framework is proposed to generate corner cases for the decision-making systems. To address the challenge brought by high dimensionality, the driving environment is formulated based on Markov Decision Process, and the deep reinforcement learning techniques are applied to learn the behavior policy of BVs. With the learned policy, BVs will behave and interact with the CAVs more aggressively, resulting in more corner cases. To further analyze the generated corner cases, the techniques of feature extraction and clustering are utilized. By selecting representative cases of each cluster and outliers, the valuable corner cases can be identified from all generated corner cases. Simulation results of a highway driving environment show that the proposed methods can effectively generate and identify the valuable corner cases.

Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing

Microscopic traffic simulation provides a controllable, repeatable, and efficient testing environment for autonomous vehicles (AVs). To evaluate AVs' safety performance unbiasedly, ideally, the probability distributions of the joint state space of all vehicles in the simulated naturalistic driving environment (NDE) needs to be consistent with those from the real-world driving environment. However, although human driving behaviors have been extensively investigated in the transportation engineering field, most existing models were developed for traffic flow analysis without consideration of distributional consistency of driving behaviors, which may cause significant evaluation biasedness for AV testing. To fill this research gap, a distributionally consistent NDE modeling framework is proposed. Using large-scale naturalistic driving data, empirical distributions are obtained to construct the stochastic human driving behavior models under different conditions, which serve as the basic behavior models. To reduce the model errors caused by the limited data quantity and mitigate the error accumulation problem during the simulation, an optimization framework is designed to further enhance the basic models. Specifically, the vehicle state evolution is modeled as a Markov chain and its stationary distribution is twisted to match the distribution from the real-world driving environment. In the case study of highway driving environment using real-world naturalistic driving data, the distributional accuracy of the generated NDE is validated. The generated NDE is further utilized to test the safety performance of an AV model to validate its effectiveness.

Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. In Part I of the paper, a general framework is proposed to solve the testing scenario library generation (TSLG) problem with four associated research questions. The methodologies of solving each research question have been proposed and analyzed theoretically. In Part II of the paper, three case studies are designed and implemented to demonstrate the proposed methodologies. First, a cut-in case is designed for safety evaluation and to provide answers to three particular questions in the framework, i.e., auxiliary objective function design, naturalistic driving data (NDD) analysis, and surrogate model (SM) construction. Second, a highway exit case is designed for functionality evaluation. Third, a car-following case is designed to show the ability of the proposed methods in handling high-dimensional scenarios. To address the challenges brought by higher dimensions, the proposed methods are enhanced by reinforcement learning (RL) techniques. Typical CAV models are chosen and evaluated by simulations. Results show that the proposed methods can accelerate the CAV evaluation process by $255$ to $3.75\times10^5$ times compared with the public road test method, with same accuracy of indices.