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.
@article{arxiv.2405.05354,
title = {Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios},
author = {Chirag Parikh and Ravi Shankar Mishra and Rohan Chandra and Ravi Kiran Sarvadevabhatla},
journal= {arXiv preprint arXiv:2405.05354},
year = {2024}
}