Deep Kinematic Models for Physically Realistic Prediction of Vehicle Trajectories

While the trajectory without the vehicle model appears reasonable, it is physically impossible for a two-axle vehicle to execute its motion in such manner because its rear wheels cannot turn. The proposed approach outputs a trajectory that is kinematically feasible and correctly predicts that the actor will encroach into the neighboring lane. We summarize the main contributions of our work below: - We combine powerful deep methods with a kinematic two-axle vehicle motion model in order to output trajectory predictions with guaranteed physical realism; - While the idea is general and applicable to any deep architecture, we present an example application to a recently proposed state-of-the-art motion prediction method, using raster-ized images of vehicle context as input to convolutional neural networks (CNNs) [7]; - We evaluate the method on a large-scale, real-world data set collected by a fleet of SDVs, showing that the system provides accurate, kinematically feasible predictions that outperform the existing state-of-the-art. 2 Related work 2.1 Motion prediction in autonomous driving Accurate motion prediction of other vehicles is a critical component in many autonomous driving systems [9, 10, 11]. Prediction provides an estimate of future world state, which can be used to plan an optimal path for the SDV through a dynamic traffic environment. The current state (e.g., position, speed, acceleration) of vehicles around a SDV can be estimated using techniques such as a Kalman filter (KF) [12, 13]. A common approach for short time horizon predictions of future motion is to assume that the driver will not change any control inputs (steering, accelerator) and simply propagate vehicle's current estimated state over time using a physical model (e.g., a vehicle motion model) that captures the underlying kinematics [9]. For longer time horizons the performance of this approach degrades as the underlying assumption of constant controls becomes increasingly unlikely.