Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios
arxiv(2024)
摘要
Recognizing driving behaviors is important for downstream tasks such as
reasoning, planning, and navigation. Existing video recognition approaches work
well for common behaviors (e.g. "drive straight", "brake", "turn left/right").
However, the performance is sub-par for underrepresented/rare behaviors
typically found in tail of the behavior class distribution. To address this
shortcoming, we propose Transfer-LMR, a modular training routine for improving
the recognition performance across all driving behavior classes. We extensively
evaluate our approach on METEOR and HDD datasets that contain rich yet
heavy-tailed distribution of driving behaviors and span diverse traffic
scenarios. The experimental results demonstrate the efficacy of our approach,
especially for recognizing underrepresented/rare driving behaviors.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要