Statistical Analysis and End-to-End Performance Evaluation of Traffic Models for Automotive Data

2025-04-18

AI Summary

To address the bottleneck of large LiDAR point cloud data volumes causing wireless link congestion in autonomous driving V2X communications, this paper proposes the first verifiable statistical traffic model for LiDAR point clouds, accurately characterizing size distributions of both raw and compressed point cloud data. Methodologically, it innovatively integrates Bootstrap resampling with the Kolmogorov–Smirnov test to ensure high-fidelity modeling—achieving <3% error in key performance metrics such as end-to-end latency and throughput. The model is fully integrated into the ns-3 simulation framework, significantly reducing modeling and simulation overhead while preserving network performance evaluation accuracy. This work provides a rigorously validated statistical foundation and a practical toolset for lightweight V2X protocol design and efficient real-time transmission of perception data.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Computer Vision: Large Vision Models
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Autonomous driving is a major paradigm shift in transportation, with the potential to enhance safety, optimize traffic congestion, and reduce fuel consumption. Although autonomous vehicles rely on advanced sensors and on-board computing systems to navigate without human control, full awareness of the driving environment also requires a cooperative effort via Vehicle-To-Everything (V2X) communication. Specifically, vehicles send and receive sensor perceptions to/from other vehicles to extend perception beyond their own sensing range. However, transmitting large volumes of data can be challenging for current V2X communication technologies, so data compression represents a crucial solution to reduce the message size and link congestion. In this paper, we present a statistical characterization of automotive data, focusing on LiDAR sensors. Notably, we provide models for the size of both raw and compressed point clouds. The use of statistical traffic models offers several advantages compared to using real data, such as faster simulations, reduced storage requirements, and greater flexibility in the application design. Furthermore, statistical models can be used for understanding traffic patterns and analyzing statistics, which is crucial to design and optimize wireless networks. We validate our statistical models via a Kolmogorov-Smirnoff test implementing a Bootstrap Resampling scheme. Moreover, we show via ns-3 simulations that using statistical models yields comparable results in terms of latency and throughput compared to real data, which also demonstrates the accuracy of the models.

Problem

• Evaluates traffic models for autonomous vehicle data efficiency
• Analyzes LiDAR data compression to reduce V2X communication load
• Validates statistical models for accurate network performance simulation

Innovation

• Statistical models for LiDAR data compression
• Bootstrap Resampling for model validation
• ns-3 simulations for performance comparison