Spatial and social situation-aware transformer-based trajectory prediction of autonomous systems
University of Duisburg-Essen（杜伊斯堡-埃森大学）；Institute of Ship Technology, Ocean Engineering and Transport Systems（船舶技术、海洋工程与交通系统研究所）；Chair of Dynamics and Control（动力学与控制讲席）
Kathrin Donandt, Dirk Söffker
发表信息：
Proceedings of the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Honolulu, Oahu, HI, USA, pp. 4488-4493
Autonomous transportation systems such as road vehicles or vessels require the consideration of the static and dynamic environment to dislocate without collision. Anticipating the behavior of an agent in a given situation is required to adequately react to it in time. Developing deep learning-based models has become the dominant approach to motion prediction recently. The social environment is often considered through a CNN-LSTM-based sub-module processing a $\textit{social tensor}$ that includes information of the past trajectory of surrounding agents. For the proposed transformer-based trajectory prediction model, an alternative, computationally more efficient social tensor definition and processing is suggested. It considers the interdependencies between target and surrounding agents at each time step directly instead of relying on information of last hidden LSTM states of individually processed agents. A transformer-based sub-module, the Social Tensor Transformer, is integrated into the overall prediction model. It is responsible for enriching the target agent's dislocation features with social interaction information obtained from the social tensor. For the awareness of spatial limitations, dislocation features are defined in relation to the navigable area. This replaces additional, computationally expensive map processing sub-modules. An ablation study shows, that for longer prediction horizons, the deviation of the predicted trajectory from the ground truth is lower compared to a spatially and socially agnostic model. Even if the performance gain from a spatial-only to a spatial and social context-sensitive model is small in terms of common error measures, by visualizing the results it can be shown that the proposed model in fact is able to predict reactions to surrounding agents and explicitely allows an interpretable behavior.
FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Driving
College of Automotive and Energy Engineering, Tongji University（同济大学汽车与能源工程学院）；College of Transportation Engineering, Tongji University（同济大学交通工程学院）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）
Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
Khalifa University（哈利法大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Khalifa University, KUCARS-KU Center for Autonomous Robotic Systems（哈利法大学KUCARS自主机器人系统中心）
