Understanding Driving Risks via Prompt Learning
Yubo Chang, Fan Lyu, Zhang Zhang, Liang Wang · 2024
Understanding driving risks is crucial for enhancing driving safety. It is a challenging task to evaluate driving risks in various complex driving scenarios. Inspired by prompt-based learning, we propose an end-to-end approach for identifying the highest-risk object in the current driving scenario based on a learnable risk pool. Specifically, a method based on key-value pair matching is designed to build a memory system for learning a collection of risk prototypes. Extensive experiments on the DRAMA dataset show that the proposed method achieves an improvement of 18.6% in Mean-IOU and 3.0% in B4 score compared to the state-of-the-art (SOTA) methods, which indicates that our method can effectively localize risky objects and accurately describe the driving scenes.