Abstract
Predicting the future trajectories of surrounding vehicles plays a vital role in ensuring the safety of autonomous driving. It is extremely challenging for the pure imitation method due to the high degree of multimodality and uncertainty in the future. In fact, when driving in most traffic scenarios, vehicles should obey some traffic rules such as “vehicles follow the lane and do not collide with each other”. Inspired by this, this paper proposes a goal-aware prediction (GAP) framework to predict the multimodal trajectories, where goals are chosen in the lanes with hierarchical interactive representation and a multi-task loss. Based on the graph-based vectorized input, a novel hierarchical interactive representation module is first designed to obtain the fine-grained goal features, which progressively models interactions between goal-to-goal, goal-to-lane, and lane-to-agent, corresponding to the individual, local and global levels, respectively. Then, an auxiliary collision loss is developed to take into account learning from demonstration and injecting common sense of collision avoidance, and is served as a part of the multi-task loss to guide the generation of multimodal plausible trajectories. In the end, the proposed method is verified on the Baidu In-house Cut-in dataset, which includes more than 370K interactive scenarios collected in the real road testing. The comparative results demonstrate the superior performance of our proposed GAP model than the mainstream prediction methods.
This work was supported by the National Natural Science Foundation of China (NSFC) under Grants No. 62173325, and also was supported by the Beijing Municipal Natural Science Foundation under Grants L191002.
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Li, D., Zhang, Q., Lu, S., Pan, Y., Zhao, D. (2022). GAP: Goal-Aware Prediction with Hierarchical Interactive Representation for Vehicle Trajectory. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1744. Springer, Singapore. https://doi.org/10.1007/978-981-19-9297-1_22
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