Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions

Abstract

Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to positively affect the quality of life of millions of people. To unlock this potential one of the critical aspects of the autonomous technology is understanding and predicting future movement of vehicles surrounding the SDV. This work presents a deep-learning-based method for kinematically feasible motion prediction of such traffic actors. Previous work did not explicitly encode vehicle kinematics and instead relied on the models to learn the constraints directly from the data, potentially resulting in kinematically infeasible, suboptimal trajectory predictions. To address this issue we propose a method that seamlessly combines ideas from the AI with physically grounded vehicle motion models. In this way we employ best of the both worlds, coupling powerful learning models with strong feasibility guarantees for their outputs. The proposed approach is general, being applicable to any type of learning method. Extensive experiments using deep convnets on real-world data strongly indicate its benefits, outperforming the existing state-of-the-art.

arXiv commentAccepted for publication at IEEE International Conference on Robotics and Automation (ICRA) 2020

References in corpus (8)

• Adam: A Method for Stochastic Optimization
• Sequence to Sequence Learning with Neural Networks
• Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
• IntentNet: Learning to Predict Intention from Raw Sensor Data
• Differentiable MPC for End-to-end Planning and Control
• Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network
• Visual Interaction Networks
• Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving

Cited by in corpus (4)

• What-If Motion Prediction for Autonomous Driving
• Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization
• Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures
• ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots