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• W2975065512 abstract "Accurately forecasting the future movements of surrounding vehicles is essential for safe and efficient operations of autonomous driving cars. This task is difficult because a vehicle's moving trajectory is greatly determined by its driver's intention, which is often hard to estimate. By leveraging attention mechanisms along with long short-term memory (LSTM) networks, this work learns the relation between a driver's intention and the vehicle's changing positions relative to road infrastructures, and uses it to guide the prediction. Different from other state-of-the-art solutions, our work treats the on-road lanes as non-Euclidean structures, unfolds the vehicle's moving history to form a spatio-temporal graph, and uses methods from Graph Neural Networks to solve the problem. Not only is our approach a pioneering attempt in using non-Euclidean methods to process static environmental features around a predicted object, our model also outperforms other state-of-the-art models in several metrics. The practicability and interpretability analysis of the model shows great potential for large-scale deployment in various autonomous driving systems in addition to our own." @default.
• W2975065512 created "2019-10-03" @default.
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• W2975065512 date "2019-09-29" @default.
• W2975065512 modified "2023-09-24" @default.
• W2975065512 title "Lane Attention: Predicting Vehicles' Moving Trajectories by Learning Their Attention over Lanes" @default.
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• W2975065512 doi "https://doi.org/10.48550/arxiv.1909.13377" @default.
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