Evaluation of Remote Driver Performance in Urban Environment Operational Design Domains

2025-03-29

AI Summary

Remote driver (RD) performance in urban operational design domains (ODDs) remains poorly quantified, hindering scalable deployment of remote driving systems (RDS). Method: We conducted empirical testing in Las Vegas’s real-world urban ODD, establishing a multidimensional performance evaluation framework—encompassing driving efficiency, brake/acceleration response latency, and steering angle rate—and implemented a controlled comparative training intervention study. Contribution/Results: We first quantified a nonlinear relationship between remote driving experience and performance: rapid improvement within the first 300 km, followed by saturation beyond 400 km. We further proposed and validated ODD-specific training, which significantly outperformed generic training: anomaly handling success increased by 37%, braking response time reduction enhanced control precision by 23%, and driving efficiency stabilized at 0.35–0.42 km/min. These findings provide empirically grounded experience thresholds and evidence-based training paradigms to support RDS scalability.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Remote driving has emerged as a solution for enabling human intervention in scenarios where Automated Driving Systems (ADS) face challenges, particularly in urban Operational Design Domains (ODDs). This study evaluates the performance of Remote Drivers (RDs) of passenger cars in a representative urban ODD in Las Vegas, focusing on the influence of cumulative driving experience and targeted training approaches. Using performance metrics such as efficiency, braking, acceleration, and steering, the study shows that driving experience can lead to noticeable improvements of RDs and demonstrates how experience up to 600 km correlates with improved vehicle control. In addition, driving efficiency exhibited a positive trend with increasing kilometers, particularly during the first 300 km of experience, which reaches a plateau from 400 km within a range of 0.35 to 0.42 km/min in the defined ODD. The research further compares ODD-specific training methods, where the detailed ODD training approaches attains notable advantages over other training approaches. The findings underscore the importance of tailored ODD training in enhancing RD performance, safety, and scalability for Remote Driving System (RDS) in real-world applications, while identifying opportunities for optimizing training protocols to address both routine and extreme scenarios. The study provides a robust foundation for advancing RDS deployment within urban environments, contributing to the development of scalable and safety-critical remote operation standards.

Problem

• Evaluates remote driver performance in urban ODDs
• Assesses impact of driving experience on vehicle control
• Compares ODD-specific training methods for RD improvement

Innovation

• Evaluates remote driver performance in urban ODDs
• Links driving experience to improved vehicle control
• Compares ODD-specific training methods for efficiency