Multi-Vehicle Collaborative Learning for Trajectory Prediction With Spatio-Temporal Tensor Fusion

Accurate behavior prediction of other vehicles in the surroundings is critical for intelligent transportation systems. Common practices to reason about the future trajectory are through their historical paths. However, the impact of traffic context is ignored, which means the beneficial environment information is deserted. Although a few methods are proposed to exploit the surrounding vehicle information, they simply model the influence according to spatial relations without considering the temporal information among them. In this paper, a novel multi-vehicle collaborative learning with spatio-temporal tensor fusion model for vehicle trajectory prediction is proposed, which introduces a novel auto-encoder social convolution mechanism and a fancy recurrent social mechanism to model spatial and temporal information among multiple vehicles, respectively. Furthermore, the generative adversarial network is incorporated into our framework to handle the inherent multi-modal characteristics of the agent motion behavior. Finally, we evaluate the proposed multi-vehicle collaborative learning model on NGSIM US-101 and I-80 benchmark datasets. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art for vehicle trajectory prediction. Additionally, we also present qualitative analyses of the multi-modal vehicle trajectory generation and the impacts of surrounding vehicles on trajectory prediction under various circumstances.

I. Introduction

Accurate and efficient trajectory prediction for intelligent vehicles is significant in sophisticated traffic scenarios where various vehicles and human crowds travel towards respective destinations with distinctive moving patterns [1], [2]. An intelligent vehicle is expected to be capable of taking actions proactively when encountering some emergencies, such as slowing down to enable surrounding intelligent vehicles to inlet and speeding up to switch lanes for overtaking. Consequently, intelligent vehicles are required to reason about accurate future trajectories of adjacent vehicles in order to conduct risk assessments of vehicle behaviors and further take appropriate actions.