2021/09/15 by Benedikt Mersch, Thomas Höllen, Mersch, Benedikt +7 · 2 citations
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Artificial intelligence #Autonomous Vehicle Technology and Safety #Computer science #Computer security #Convolutional neural network #Engineering #Exploit #FOS: Computer and information sciences #Interdependence #Key (lock) #Machine learning #Motion (physics) #Representation (politics) #Robotics (cs.RO) #Task (project management) #Trajectory #Video Surveillance and Tracking Methods #cs.RO
paper · pdf · doi:10.48550/arxiv.2109.07365
published in arXiv (Cornell University) (Cornell University) · Accepted for IROS 2021
arxiv created 2021/09/15 · openalex publication_date 2021/09/15 · arxiv updated 2021/09/16 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/08
The ability to predict the future movements of other vehicles is a subconscious and effortless skill for humans and key to safe autonomous driving. Therefore, trajectory prediction for autonomous cars has gained a lot of attention in recent years. It is, however, still a hard task to achieve human-level performance. Interdependencies between vehicle behaviors and the multimodal nature of future intentions in a dynamic and complex driving environment render trajectory prediction a challenging problem. In this work, we propose a new, data-driven approach for predicting the motion of vehicles in a road environment. The model allows for inferring future intentions from the past interaction among vehicles in highway driving scenarios. Using our neighborhood-based data representation, the proposed system jointly exploits correlations in the spatial and temporal domain using convolutional neural networks. Our system considers multiple possible maneuver intentions and their corresponding motion and predicts the trajectory for five seconds into the future. We implemented our approach and evaluated it on two highway datasets taken in different countries and are able to achieve a competitive prediction performance.