Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network

• URL: http://arxiv.org/abs/2103.16273v1
• Date: Tue, 30 Mar 2021 11:53:12 GMT
• Title: Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network
• Authors: Bo Dong, Hao Liu, Yu Bai, Jinbiao Lin, Zhuoran Xu, Xinyu Xu, Qi Kong
• Abstract summary: There are several challenges in trajectory prediction in real-world traffic scenarios. Inspired by people's natural habit of navigating traffic with attention to their goals and surroundings, this paper presents a unique graph attention network. The network is designed to model the dynamic social interactions among agents and conform to traffic rules with a semantic map.
• Score: 12.791191495432829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting future trajectories of surrounding obstacles is a crucial task for autonomous driving cars to achieve a high degree of road safety. There are several challenges in trajectory prediction in real-world traffic scenarios, including obeying traffic rules, dealing with social interactions, handling traffic of multi-class movement, and predicting multi-modal trajectories with probability. Inspired by people's natural habit of navigating traffic with attention to their goals and surroundings, this paper presents a unique dynamic graph attention network to solve all those challenges. The network is designed to model the dynamic social interactions among agents and conform to traffic rules with a semantic map. By extending the anchor-based method to multiple types of agents, the proposed method can predict multi-modal trajectories with probabilities for multi-class movements using a single model. We validate our approach on the proprietary autonomous driving dataset for the logistic delivery scenario and two publicly available datasets. The results show that our method outperforms state-of-the-art techniques and demonstrates the potential for trajectory prediction in real-world traffic.

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