Performance-Sensitive Potential Functions for Efficient Flow of Connected and Automated Vehicles

TL;DR

This paper introduces performance-sensitive potential functions for coordinating connected and automated vehicles, optimizing for safety and efficiency while ensuring real-time applicability through neural network approximation.

Contribution

It presents a novel optimization-based method for tuning potential functions and employs neural networks to enable real-time control of CAVs.

Findings

Neural network successfully approximates optimal potential parameters.

Proposed method improves safety and efficiency in vehicle coordination.

Simulation results validate the effectiveness of the approach.

Abstract

Connected and automated vehicles (CAVs) provide the most intriguing opportunity for enabling users to monitor transportation network conditions and make better decisions for improving safety and transportation efficiency. In this paper, we address the problem of effectively coordinating CAVs on lane-based roadways. Our approach utilizes potential functions to generate repulsive forces between CAVs that ensure collision avoidance. However, such potential functions can lead to unrealistic acceleration profiles and large inter-vehicle distances. The primary contribution of this work is the introduction of performance-sensitive potential functions to address these challenges. In our approach, the parameters of a potential function are determined through an optimization problem aiming to reduce both acceleration and inter-vehicle distances. To circumvent the computational implications due to the complexity of the resulting optimization problem that prevents the derivation of a real-time solution, we train a neural network model to learn the mapping of initial conditions to optimal parameters derived offline. Then, we prove sufficient criteria for the sampled-data model to ensure that the neural network output does not activate any of the state and safety constraints. Finally, we provide simulation results to demonstrate the effectiveness of the proposed approach.