Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic. Motion of all vehicles in a scene is governed by the traffic context, i.e., the motion and relative spatial configuration of neighboring vehicles. In this paper we propose an LSTM encoder-decoder model that uses convolutional social pooling as an improvement to social pooling layers for robustly learning interdependencies in vehicle motion. Additionally, our model outputs a multi-modal predictive distribution over future trajectories based on maneuver classes. We evaluate our model using the publicly available NGSIM US-101 and I-80 datasets. Our results show improvement over the state of the art in terms of RMS values of prediction error and negative log-likelihoods of true future trajectories under the model's predictive distribution. We also present a qualitative analysis of the model's predicted distributions for various traffic scenarios.
Considering that the LSTM model fails to capture the interdependencies of the motion of all cars in the scene, they then enhance their scheme by adding convolutional social pooling layers in REF .
Convolutional Social Pooling for Vehicle Trajectory Prediction
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 1468-1476
Computer Science