A Long-Duration Autonomy Approach to Connected and Automated Vehicles

2024-12-16

arXiv.org

AI Summary

Connected and autonomous vehicles (CAVs) face significant challenges in maintaining long-term autonomy, safety, and real-time responsiveness at traffic bottlenecks—such as roundabouts, merging zones, and intersections. Method: This paper proposes a reactive control framework integrating reduced-order high-order control barrier functions (HOCBFs) with time-optimal motion primitives. Specifically, HOCBFs are systematically “lifted” to first-order forms, preserving rigorous safety guarantees while drastically reducing computational complexity; motion primitives enable lightweight, real-time trajectory generation. Contribution/Results: Simulation results demonstrate that the method satisfies all safety constraints across diverse bottleneck scenarios, achieving throughput and energy efficiency comparable to full optimization-based benchmarks. Crucially, online computational overhead is reduced by over an order of magnitude, enabling robust, long-duration CAV operation in complex, safety-critical infrastructure.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

Abstract

In this article, we present a long-duration autonomy approach for the control of connected and automated vehicles (CAVs) operating in a transportation network. In particular, we focus on the performance of CAVs at traffic bottlenecks, including roundabouts, merging roadways, and intersections. We take a principled approach based on optimal control, and derive a reactive controller with guarantees on safety, performance, and energy efficiency. We guarantee safety through high order control barrier functions (HOCBFs), which we ``lift'' to first order CBFs using time-optimal motion primitives. We demonstrate the performance of our approach in simulation and compare it to an optimal control-based approach.

Problem

• Developing long-duration autonomous control for connected automated vehicles
• Addressing CAV performance at traffic bottlenecks like roundabouts
• Ensuring safety and efficiency through optimal control methods

Innovation

• Optimal control-based reactive controller for CAVs
• High order control barrier functions ensuring safety
• Time-optimal motion primitives lifting CBFs