Can I read DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data on EtoBox?

DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data by Lu, Chengjie; Yue, Tao; Zhang, Man; Ali, Shaukat is a scholarly article available to read on EtoBox.

What is DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data about?

Autonomous driving systems (ADSs) are capable of sensing the environment and making driving decisions autonomously. These systems are safety-critical, and testing them is one of the important approaches to ensure their safety. However, due to the inherent complexity of ADSs and the high dimensionality of their operating environment, the number of possible test scenarios for ADSs is infinite. Besides, the operating environment of ADSs is dynamic, continuously evolving, and full of uncertainties, which requires a testing approach adaptive to the environment. In addition, existing ADS testing techniques have limited effectiveness in ensuring the realism of test scenarios, especially the realism of weather conditions and their changes over time. Recently, reinforcement learning (RL) has demonstrated great potential in addressing challenging problems, especially those requiring constant adaptations to dynamic environments. To this end, we present DeepQTest, a novel ADS testing approach that uses RL to learn environment configurations with a high chance of revealing abnormal ADS behaviors. Specifically, DeepQTest employs Deep Q-Learning and adopts three safety and comfort measures to con

Author

Lu, Chengjie; Yue, Tao; Zhang, Man; Ali, Shaukat

Published

2023

Language

EN