Trajectory Prediction of Heterogeneous Traffic Agents With Collision Vigilance and Avoidance

Having knowledge of neighboring agents' motion patterns along with collision probability is a key challenge for heterogeneous agents' trajectory prediction. In this article, we present rich relational feature learning for efficacious and safe traffic agents' motion forecast. The global temporal information is leveraged using co-attention in feature space. The proposed model not only considers observed trajectories and agents' relational patterns but also themodel is learned to be conscious of collision likelihood. The extent of collision likelihood is computed for each agent's move and guided to long short-term memory network during model training. Ground-truth information about the collision alertness among neighboring nodes' trajectories is not available, therefore, reinforcement learning is employed for learning this task. Extensive evaluation results on Apolloscape and Argoverse benchmark datasets are conducted. Asubstantial performance improvement of the proposed method over the state-of-the-art methods is achieved in terms of average displacement error and the final displacement error.