Deep Stochastic Kinematic Models for Probabilistic Motion Forecasting in Traffic: Related Work

Table of Links

Abstract and I. Introduction

II. Related Work

III. Kinematics of Traffic Agents

IV. Methodology

V. Results

VI. Discussion and Conclusion, and References

VII. Appendix

II. RELATED WORKS

A. Trajectory Forecasting for Traffic

Traffic trajectory forecasting is a popular task where the goal is to predict the short-term future trajectory of multiple agents in a traffic scene. Being able to predict the future positions and intents of each vehicle provides context for other modules in autonomous driving, such as path planning. Large, robust benchmarks such as the Waymo Motion Dataset [8], [9], Argoverse [10], and the NuScenes Dataset [11] have provided a standardized setting for advancements in the task, with leaderboards showing clear rankings for state-of-the-art models. Amongst the top performing architectures, most are based on Transformers for feature extraction [7], [12], [13], [14], [15]. Current SOTA models also model trajectory prediction probabilistically, as inspired by the use of GMMs in MultiPath [16].

One common theme amongst relevant state-of-the-art, however, is that works employing kinematic models for time-integrated trajectory rollouts typically only consider kinematic variables deterministically, which neglect the relation between kinematic input uncertainty and trajectory rollout uncertainty [1], [4], [17]. In our work, we present a method for use of kinematic priors which can be complemented with any previous work in trajectory forecasting. Our contribution can be implemented in any of the SOTA methods above, since it is a simple reformulation of the task with no additional information needed.

B. Physics-based Priors for Learning

Model-based learning has shown to be effective in many applications, especially in robotics and graphics. There are generally two approaches to using models of the real world: 1) learning a model of dynamics via a separate neural network [18], [19], [20], [21], or 2) using existing models of the real world via differentiable simulation [22], [23], [24], [25], [26], [27], [28].

In our method, we pursue the latter. Since we are not modeling complex systems such as cloth or fluid, simulation of traffic agent states require only a simple, fast, and differentiable update. In addition, since the kinematic models do not describe interactions between agents, the complexity of the necessary model is greatly reduced. In this paper, we hypothesize that modeling the simple kinematics (e.g., how a vehicle moves forward) with equations will allow for greater modeling expressivity on behavior.