Abstract: 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.

Dataset

The large-scale dataset consists of synchronized Labeled image and LiDAR scanned point clouds. It captured by HESAI Pandora All-in-One Sensing Kit. It can be used for inpainting and other tasks. [Download]

Publication

TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents. 

Yuexin Ma, Xinzhe Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, Dinesh Manocha. 

The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019 (Oral)). (acceptance rate: 16%) 

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Media:

百度15篇论文被AAAI 2019收录