Spatiotemporal Receding Horizon Control with Proactive Interaction Towards Autonomous Driving in Dense Traffic

TL;DR

This paper introduces a computationally-efficient spatiotemporal receding horizon control scheme for autonomous driving in dense traffic, emphasizing safety, energy efficiency, and proactive interaction modeling to improve real-time trajectory planning.

Contribution

It presents a novel ST-RHC framework with an embodied safety barrier module for proactive vehicle interaction, enhancing safety and accuracy in dense traffic scenarios.

Findings

Outperforms state-of-the-art algorithms in accuracy, safety, and efficiency.

Demonstrates effectiveness on synthetic and real-world traffic datasets.

Provides a real-time, energy-efficient trajectory planning solution.

Abstract

In dense traffic scenarios, ensuring safety while keeping high task performance for autonomous driving is a critical challenge. To address this problem, this paper proposes a computationally-efficient spatiotemporal receding horizon control (ST-RHC) scheme to generate a safe, dynamically feasible, energy-efficient trajectory in control space, where different driving tasks in dense traffic can be achieved with high accuracy and safety in real time. In particular, an embodied spatiotemporal safety barrier module considering proactive interactions is devised to mitigate the effects of inaccuracies resulting from the trajectory prediction of other vehicles. Subsequently, the motion planning and control problem is formulated as a constrained nonlinear optimization problem, which favorably facilitates the effective use of off-the-shelf optimization solvers in conjunction with multiple shooting. The effectiveness of the proposed ST-RHC scheme is demonstrated through comprehensive comparisons with state-of-the-art algorithms on synthetic and real-world traffic datasets under dense traffic, and the attendant outcome of superior performance in terms of accuracy, efficiency and safety is achieved.