Survey on Traffic Flow-Based Autonomous Driving Simulation Tests

Autonomous driving simulation tests play a crucial role in advancing autonomous driving technology and assessing its level of development. Conducting tests that incorporate traffic flow brings several advantages, including a comprehensive representation of various traffic elements and a close resemblance to real-world driving conditions. Such tests are effective in evaluating the performance of autonomous vehicles under different traffic scenarios. However, traditional traffic flow simulations primarily concentrate on enhancing the overall efficiency of the traffic system, rather than catering specifically to the testing requirements of autonomous vehicles within traffic flow. As a result, there is currently a shortage of comprehensive research summarizing the testing of autonomous vehicles in traffic flow environments. To address this issue, existing traffic models describing specific vehicle behavioral models and traffic flow-based autonomous driving test methods and metrics are first classified and summarized. In addition, future research on traffic flow-based autonomous driving tests is highlighted and discussed. By systematically organizing existing knowledge and highlighting potential avenues for research, this work aims to bridge the gap in understanding and facilitate the progress of autonomous driving testing within traffic flow environments.