Vehicle Routing Problems in the Age of Semi-Autonomous Driving

2025-02-05

AI Summary

This paper addresses the Vehicle Routing Problem under Semi-Autonomous (SA) operations (VRP-SA), where only a subset of the road network supports Autonomous Vehicles (AVs), and scarce resources—such as remote supervision—are subject to budget constraints. We first formalize VRP-SA as a Resource-Constrained Mixed-Integer Linear Program (MILP). To solve it exactly, we propose a two-stage framework centered on a Feasibility Restoration Problem (FRP), jointly optimizing AV segment utilization and resource allocation. Our approach innovatively integrates hierarchical grid-based road network modeling with customized heuristics. On benchmark instances, our method reduces total service cost by up to 37.5% compared to baseline approaches; cost savings scale significantly with increased resource budgets and extended operational horizons. These results demonstrate both the practical efficacy and scalability of our framework for real-world SA deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We are in the midst of a semi-autonomous era in urban transportation in which varying forms of vehicle autonomy are gradually being introduced. This phase of partial autonomy is anticipated by some to span a few decades due to various challenges, including budgetary constraints to upgrade the infrastructure and technological obstacles in the deployment of fully autonomous vehicles (AV) at scale. In this study, we introduce the vehicle routing problem in a semi-autonomous environment (VRP-SA) where the road network is not fully AV-enabled in the sense that a portion of it is either not suitable for AVs or requires additional resources in real-time (e.g., remote control) for AVs to pass through. Moreover, such resources are scarce and usually subject to a budget constraint. An exact mixed-integer linear program (MILP) is formulated to minimize the total routing cost of service in this environment. We propose a two-phase algorithm based on a family of feasibility recovering sub-problems (FRP) to solve the VRP-SA efficiently. Our algorithm is implemented and tested on a new set of instances that are tailored for the VRP-SA by adding stratified grid road networks to the benchmark instances. The result demonstrates a reduction of up to 37.5% in vehicle routing costs if the fleet actively exploits the AV-enabled roads in the environment. Additional analysis reveals that cost reduction is higher with more budget and longer operational hours.

Problem

• Vehicle routing in semi-autonomous environments
• Minimizing routing costs with AV-enabled roads
• Budget constraints on autonomous vehicle resources

Innovation

• Mixed-integer linear programming
• Two-phase algorithm
• Feasibility recovering sub-problems