Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction
It addresses practical deployment challenges for researchers and engineers in autonomous driving, but is incremental as it focuses on documenting experiences rather than introducing new methods.
This study tackled lane change prediction by deploying a cooperative perception architecture in real hardware within mixed traffic, revealing practical challenges like bottlenecks and reliability issues, and providing implementation insights without reporting specific numerical results.
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.