Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios
This addresses a domain-specific problem for autonomous driving systems by improving recognition of underrepresented behaviors, though it appears incremental as it builds on existing video recognition approaches.
The paper tackled the problem of poor recognition performance for rare driving behaviors in video recognition systems, proposing Transfer-LMR to improve recognition across all classes, with experimental results showing efficacy on METEOR and HDD datasets.
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.