Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles

2022/07/02 by Weitao Zhou, Zhou, Weitao, Zhong Cao +10 · 4 citations
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Traffic Prediction and Management Techniques

paper · pdf · doi:10.48550/arxiv.2207.00788

openalex publication_date 2022/07/02 · openalex created_date 2022/07/07 · openalex updated_date 2026/07/28

Abstract

A typical trajectory planner of autonomous driving commonly relies on predicting the future behavior of surrounding obstacles. Recently, deep learning technology has been widely adopted to design prediction models due to their impressive performance. However, such models may fail in the "long-tail" driving cases where the training data is sparse or unavailable, leading to planner failures. To this end, this work proposes a trajectory planner to consider the prediction model uncertainty arising from insufficient data for safer performance. Firstly, an ensemble network structure estimates the prediction model's uncertainty due to insufficient training data. Then a trajectory planner is designed to consider the worst-case arising from prediction uncertainty. The results show that the proposed method can improve the safety of trajectory planning under the prediction uncertainty caused by insufficient data. At the same time, with sufficient data, the framework will not lead to overly conservative results. This technology helps to improve the safety and reliability of autonomous vehicles under the long-tail data distribution of the real world.

Cited by

• A Trajectory Generator for High-Density Traffic and Diverse Agent-Interaction Scenarios
• PIE: Perception and Interaction Enhanced End-to-End Motion Planning for Autonomous Driving
• AdaSports-Traj: Role- and Domain-Aware Adaptation for Multi-Agent Trajectory Modeling in Sports
• TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning

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