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

arXiv: https://arxiv.org/abs/2509.20218 · HTML: https://mlsanity.com/paper/2509.20218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 55 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models](https://mlsanity.com/paper/2403.18344/) (2024)
- [RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2405.00449/) (2024)
- [Video action recognition for lane-change classification and prediction of surrounding vehicles](https://mlsanity.com/paper/2101.05043/) (2021)
- [Two-Stream Networks for Lane-Change Prediction of Surrounding Vehicles](https://mlsanity.com/paper/2008.10869/) (2020)
- [Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and Bayesian Inference](https://mlsanity.com/paper/2312.06336/) (2023)
- [Testing predictive automated driving systems: lessons learned and future recommendations](https://mlsanity.com/paper/2205.10115/) (2022)
- [Attention-based Lane Change Prediction](https://mlsanity.com/paper/1903.01246/) (2019)
- [Lane Change Classification and Prediction with Action Recognition Networks](https://mlsanity.com/paper/2208.11650/) (2022)
- [Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation](https://mlsanity.com/paper/2501.11560/) (2025)
- [Real-World Deployment of a Lane Change Prediction Architecture Based on Knowledge Graph Embeddings and Bayesian Inference](https://mlsanity.com/paper/2506.11925/) (2025)

## Built on by (most cited first, 1 of 1)

- [INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis](https://mlsanity.com/paper/2607.08316/) (2026)

## Abstract (arXiv)

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.