Parallel Optimization with Hard Safety Constraints for Cooperative Planning of Connected Autonomous Vehicles

TL;DR

This work addresses cooperative planning for $N$ CAVs at unsignalized roundabouts under hard safety constraints by formulating a constrained optimal control problem and delivering a convex reformulation through linearization of collision and boundary constraints. It introduces an implicit passing sequence mechanism via iterative nearest-neighbor reference updates and solves the resulting convex problem in parallel using a dual-consensus ADMM framework, with per-vehicle LQR-like subproblems and explicit dual/primal updates. The method is validated in CARLA Town03 with $N=16$ CAVs, showing improved travel efficiency (average speeds near the reference $v_{ref}$) and substantial computational speedups compared to a rule-based baseline and IPOPT. The combination of hard safety guarantees, convex reformulation, and scalable parallel optimization supports real-time cooperative planning in dense urban traffic and has potential for extensions to mixed traffic and higher-level decision making.

Abstract

The development of connected autonomous vehicles (CAVs) facilitates the enhancement of traffic efficiency in complicated scenarios. In unsignalized roundabout scenarios, difficulties remain unsolved in developing an effective and efficient coordination strategy for CAVs. In this paper, we formulate the cooperative autonomous driving problem of CAVs in the roundabout scenario as a constrained optimal control problem, and propose a computationally-efficient parallel optimization framework to generate strategies for CAVs such that the travel efficiency is improved with hard safety guarantees. All constraints involved in the roundabout scenario are addressed appropriately with convex approximation, such that the convexity property of the reformulated optimization problem is exhibited. Then, a parallel optimization algorithm is presented to solve the reformulated optimization problem, where an embodied iterative nearest neighbor search strategy to determine the optimal passing sequence in the roundabout scenario. It is noteworthy that the travel efficiency in the roundabout scenario is enhanced and the computation burden is considerably alleviated with the innovation development. We also examine the proposed method in CARLA simulator and perform thorough comparisons with a rule-based baseline and the commonly used IPOPT optimization solver to demonstrate the effectiveness and efficiency of the proposed approach.

Figures (3)

• Figure 1: Linearization of collision avoidance constraints.
• Figure 2: Linearization of road boundary constraints.
• Figure 3: Simulation results of our method and baseline at time equal to 1.0 s, 3.0 s, and 5.0 s, respectively.