MJTG: A Multi-Vehicle Joint Trajectory Generator for Complex and Rare Scenarios
Ye Tian, Wentao Zheng, Yinghao Shao, He Zhang, Jian Cheng Sun · IEEE Transactions on Vehicular Technology · 2025
The advancement of Highly Automated Vehicles (HAVs) safety testing plays a crucial role in the large-scale deployment of Automated Driving Systems (ADS). Scenario-based testing has become increasingly popular due to its customizability and testing efficiency. Unfortunately, the scarcity of collected realworld scenario data constrains the construction of a high-coverage scenario library. It is of great value to make small data into big data by generating fresh new scenarios out of existing ones. A common approach is to use Data-driven Scenario Generation (DSG) methods. However, these methods suffer from some drawbacks, including the low quality of the generated scenarios (i.e., the scenario fidelity) and the lack of diversity and criticality compared to the original scenarios (i.e., the scenario directivity). These problems are mainly caused by the complex and dynamic nature of scenarios and the rarity of critical situations in naturalistic driving environment. To address those challenges, we propose a data-driven method named the Multi-vehicle Joint Trajectory Generator (MJTG), devised to selectively generate potentially feasible scenarios based on limited collected scenarios. The MJTG framework has demonstrated significant effectiveness in terms of fidelity and directivity. Generated scenarios exhibit over 85% compliance with functional check standards. Additionally, the framework excels in addressing directivity, resulting in a reduction of 0.66 seconds in the modified time-tocollision. Overall, the MJTG framework holds promising prospects in constructing high-coverage test-worthy scenario libraries and advancing the safety testing of HAVs.