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A Long-Duration Autonomy Approach to Connected and Automated Vehicles

Logan E. Beaver

TL;DR

This work develops a long-duration autonomy framework for connected and automated vehicles operating in networks with bottlenecks. It starts from an infinite-horizon optimal-control formulation and derives a reactive, decentralized controller that enforces safety via high-order control barrier functions (HOCBFs) lifted to first-order CBFs through time-optimal motion primitives, ensuring compatibility with mixed traffic and actuation bounds. The resulting policy uses a simple clamp-based control law around a velocity-tracking target, with crossing-time and rear-end safety constraints handled by CBFs, and includes strategies for infeasibility such as safe mode or schedule updates. Simulations at an unsignalized intersection show competitive energy performance and extraordinary computational efficiency (microseconds per decision) compared to a traditional optimal-control baseline, highlighting the method’s potential for real-time deployment in urban-wide, long-duration CAV operation.

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. This yields a set of first-order CBFs that are compatible with the control bounds. We demonstrate the performance of our approach in simulation and compare it to an optimal control-based approach.

A Long-Duration Autonomy Approach to Connected and Automated Vehicles

TL;DR

This work develops a long-duration autonomy framework for connected and automated vehicles operating in networks with bottlenecks. It starts from an infinite-horizon optimal-control formulation and derives a reactive, decentralized controller that enforces safety via high-order control barrier functions (HOCBFs) lifted to first-order CBFs through time-optimal motion primitives, ensuring compatibility with mixed traffic and actuation bounds. The resulting policy uses a simple clamp-based control law around a velocity-tracking target, with crossing-time and rear-end safety constraints handled by CBFs, and includes strategies for infeasibility such as safe mode or schedule updates. Simulations at an unsignalized intersection show competitive energy performance and extraordinary computational efficiency (microseconds per decision) compared to a traditional optimal-control baseline, highlighting the method’s potential for real-time deployment in urban-wide, long-duration CAV operation.

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. This yields a set of first-order CBFs that are compatible with the control bounds. We demonstrate the performance of our approach in simulation and compare it to an optimal control-based approach.

Paper Structure

This paper contains 6 sections, 4 theorems, 25 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Notation
  3. Problem Formulation
  4. Optimization-Based Control Policy
  5. Simulation Results
  6. Conclusion

Key Result

Theorem 1

As the planning horizon $T\to\infty$, the optimal unconstrained trajectory eq:mp approaches the feedback law, where $v_i^d$ is a desired steady-state velocity for CAV $i$.

Figures (3)

  • Figure 1:
  • Figure 2: Trajectories for the optimal control-based solution of malikopoulos2021optimal. Black vertical lines show the lateral crossing constraints.
  • Figure 3: CAV trajectories generated with our proposed approach. Black vertical lines show the lateral crossing constraints.

Theorems & Definitions (12)

  • Definition 1: scheduled intersection
  • Theorem 1
  • proof
  • Lemma 1
  • proof
  • Remark 1: Lemma 2 in tzortzoglou2025mixed
  • Theorem 2
  • proof
  • Remark 2
  • Theorem 3
  • ...and 2 more