4 papers
NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment
Yu Zhao, Jiangyu Pan, Tao Hu +4
The ability to accurately assess and anticipate risks in safety-critical scenarios is crucial for autonomous driving systems. While existing research has made progress in collision…
Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning
Jia Hu, Mingyue Lei, Haoran Wang +2
Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. How…
RoadGen: Generating Road Scenarios for Autonomous Vehicle Testing
Fan Yang, You Lu, Bihuan Chen +2
With the rapid development of autonomous vehicles, there is an increasing demand for scenario-based testing to simulate diverse driving scenarios. However, as the base of any drivi…
DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking
Jiahao Yu, Yihai Duan, Longfei Xu +6
Multi-scenario route ranking (MSRR) is crucial in many industrial mapping systems. However, the industrial community mainly adopts interactive interfaces to encourage users to sele…