Title | ||
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Lane-Attention - Predicting Vehicles' Moving Trajectories by Learning Their Attention Over Lanes. |
Abstract | ||
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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. |
Year | DOI | Venue |
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2020 | 10.1109/IROS45743.2020.9341233 | IROS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jiacheng Pan | 1 | 0 | 0.34 |
Hongyi Sun | 2 | 0 | 0.34 |
Kecheng Xu | 3 | 1 | 1.45 |
Yifei Jiang | 4 | 0 | 0.68 |
Xiangquan Xiao | 5 | 1 | 1.11 |
Jiangtao Hu | 6 | 7 | 2.65 |
Jinghao Miao | 7 | 1 | 2.46 |