TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
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DOI：
10.1609/aaai.v33i01.33016120
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发表时间：
2018-11
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影响因子：
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通讯作者：
Yuexin Ma;Xinge Zhu;Sibo Zhang;Ruigang Yang;Wenping Wang;Dinesh Manocha
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作者：
Yuexin Ma;Xinge Zhu;Sibo Zhang;Ruigang Yang;Wenping Wang;Dinesh Manocha
为了在复杂的城市交通中安全有效地导航，自动驾驶汽车必须对周围的交通代理（车辆、自行车、行人等）进行负责任的预测。探索不同交通智能体的运动模式并准确预测它们的未来轨迹，以帮助自主车辆做出合理的导航决策，是一项具有挑战性和关键性的任务。为了解决这个问题，我们提出了一种基于长短期内存（LSTM）的实时流量预测算法，EschericPredict。我们的方法使用一个实例层来学习实例的移动和交互，并有一个类别层来学习属于同一类型的实例的相似性，以改进预测。为了评估其性能，我们收集了一个大城市的轨迹数据集，包括不同的条件和交通密度。该数据集包括许多具有挑战性的场景，其中车辆，自行车和行人相互移动。我们在新的数据集上评估了MPEGICPredict的性能，并通过与先前的预测方法进行比较，突出了其更高的轨迹预测准确性。
To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods.