Evaluating Real-World Multi-View Vulnerabilities in Autonomous Driving Perception and Planning for Improved Safety and Regulation

Autonomous driving systems (ADS) increasingly rely on vision-based perception and learning-based decision modules, yet real-world safety depends on whether these systems remain stable as the ego vehicle continuously changes distance and viewpoint relative to roadway objects. Current evaluation practices often emphasize single images or limited viewpoints, which can mask trajectory-dependent failure modes that emerge during real driving under changing illumination, partial occlusion, and motion blur. This project studies the limitations of existing ADS through a measurement-driven, multi-view robustness evaluation framework designed to produce actionable engineering insights and evidence-based inputs for transportation safety policy. Building on a differentiable, view-consistent scene representation (3D Gaussian Splatting with view-dependent appearance modeling), the team will generate controlled, physically plausible appearance variations across realistic approach trajectories and use them as a diagnostic tool to quantify perception instability and downstream planning sensitivity. The project will deliver a reproducible set of safety-relevant scenarios and ego-vehicle approach trajectories representing how a vehicle observes the same object over time; a controllable multi-view rendering and perturbation engine built on 3D Gaussian Splatting that synthesizes viewpoint-consistent observations under bounded, physically plausible appearance variations; and a multi-view robustness evaluation methodology and benchmark protocol using trajectory-based sampling. It will produce quantitative robustness indicators summarizing perception stability and planning sensitivity, a structured taxonomy of observed failure modes, and a reproducible reporting package of metrics definitions, evaluation scripts, and documentation templates. Validation will be performed on representative research ADS models, with black-box evaluation of commercial systems where feasible and safe.

• Record URL:
  • https://smartercenter.org/about-2/projects/evaluating-real-world-multi-view-vulnerabilities-in-autonomous-driving-perception-and-planning-for-improved-safety-and-regulation/

Language

• English

Project

• Status: Active
• Funding: $200,000.00
• Contract Numbers:

  69A3552348303

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Safety and Mobility Advancements Regional Transportation and Economics Research Center

  Morgan State University
  Baltimore, MD  United States 
• Performing Organizations:

  Safety and Mobility Advancements Regional Transportation and Economics Research Center

  Morgan State University
  Baltimore, MD  United States 
• Principal Investigators:

  Yang, Xianfeng

• Start Date: 20260801
• Expected Completion Date: 20280301
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Failure analysis; Machine vision; Traffic safety; Validation; Vehicle trajectories
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01998035
• Record Type: Research project
• Source Agency: Sustainable Mobility and Accessibility Regional Transportation Equity Research Center
• Contract Numbers: 69A3552348303
• Files: UTC, RIP
• Created Date: Aug 1 2026 10:27AM