Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction

2025-09-24

AI Summary

Existing lane-change prediction research relies heavily on simulation or offline datasets, employing overly idealized models of perception, communication, and driver behavior—limiting real-world deployability. Method: This work presents the first end-to-end cooperative lane-change prediction system deployed and evaluated on a real-world hardware platform in mixed-traffic environments. It integrates inter-vehicle cooperative perception, V2X communication, and real-time multi-source data fusion. Contribution/Results: Through extensive field testing, we identify critical deployment bottlenecks—including communication latency, multi-sensor temporal synchronization, and system reliability—and systematically document engineering challenges and lessons learned. Our study empirically validates the feasibility of cooperative perception under realistic operational conditions and bridges a critical gap in practical knowledge for cooperative driving systems. It delivers a reusable methodology and technical reference for deploying AI-based automotive systems in production environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Teamwork
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Research on lane change prediction has gained attention in the last few years. Most existing works in this area have been conducted in simulation environments or with pre-recorded datasets, these works often rely on simplified assumptions about sensing, communication, and traffic behavior that do not always hold in practice. Real-world deployments of lane-change prediction systems are relatively rare, and when they are reported, the practical challenges, limitations, and lessons learned are often under-documented. This study explores cooperative lane-change prediction through a real hardware deployment in mixed traffic and shares the insights that emerged during implementation and testing. We highlight the practical challenges we faced, including bottlenecks, reliability issues, and operational constraints that shaped the behavior of the system. By documenting these experiences, the study provides guidance for others working on similar pipelines.

Problem

• Addresses lane change prediction using real hardware deployment
• Identifies practical challenges like bottlenecks and reliability issues
• Provides implementation insights for cooperative perception systems

Innovation

• Hardware-based cooperative perception architecture
• Real-world deployment in mixed traffic
• Practical implementation challenges and insights