Multimodal Trajectory Predictions for Autonomous Driving Without a Detailed Prior Map

Atsushi Kawasaki, Akihito Seki; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 3723-3732

Predicting the future trajectories of surrounding vehicles is a key competence for safe and efficient real-world autonomous driving systems. Previous works have presented deep neural network models for predictions using a detailed prior map which includes driving lanes and explicitly expresses the road rules like legal traffic directions and valid paths through intersections. Since it is unrealistic to assume the existence of the detailed prior maps for all areas, we use a map generated from only perceptual data (3D points measured by a LiDAR sensor). Such maps do not explicitly denote road rules, which makes prediction tasks more difficult. To overcome this problem, we propose a novel generative adversarial network (GAN) based framework. A discriminator in our framework can distinguish whether predicted trajectories follow road rules, and a generator can predict trajectories following it. Our framework implicitly extracts road rules by projecting trajectories onto the map via a differentiable function and training positional relations between trajectories and obstacles on the map. We also extend our framework to multimodal predictions so that various future trajectories are predicted. Experimental results show that our method outperforms other state-of-the-art methods in terms of trajectory errors and the ratio of trajectories that fall on drivable lanes.