Identification and Classification of Human Performance related Challenges during Remote Driving
Ole Hans, Jürgen Adamy
TL;DR
This paper investigates human performance-related challenges faced by Remote Drivers (RDs) during remote driving in real-world Las Vegas conditions, analyzing 183 Safety Driver (SD) interventions, 20 harsh driving events, and RD questionnaire data. It employs a three-stage methodology to identify challenging scenarios, develops a severity framework for SD interventions, and demonstrates a pronounced learning curve as RD experience increases, with reductions in late-braking, harsh acceleration, and risky steering. Key findings include the critical early phase (first $200$–$500$ km) where interventions are most frequent, significant differences in intervention rates across RD experience levels, and the impact of system limitations such as latency and reduced haptic feedback on RD performance. The study informs targeted RD training, improvements in remote Driving System Human-Machine Interfaces (HMIs), and safety mechanisms to enhance controllability and safety in remote driving applications.
Abstract
Remote driving of vehicles is gaining in importance in the transportation sector, especially when Automated Driving Systems (ADSs) reach the limits of their system boundaries. This study investigates the challenges faced by human Remote Drivers (RDs) during remote driving, particularly focusing on the identification and classification of human performance-related challenges through a comprehensive analysis of real-world remote driving data Las Vegas. For this purpose, a total of 183 RD performance-related Safety Driver (SD) interventions were analyzed and classified using an introduced severity classification. As it is essential to prevent the need for SD interventions, this study identified and analyzed harsh driving events to detect an increased likelihood of interventions by the SD. In addition, the results of the subjective RD questionnaire are used to evaluate whether the objective metrics from SD interventions and harsh driving events can also be confirmed by the RDs and whether additional challenges can be uncovered. The analysis reveals learning curves, showing a significant decrease in SD interventions as RD experience increases. Early phases of remote driving experience, especially below 200 km of experience, showed the highest frequency of safety-related events, including braking late for traffic signs and responding impatiently to other traffic participants. Over time, RDs follow defined rules for improving their control, with experience leading to less harsh braking, acceleration, and steering maneuvers. The study contributes to understanding the requirements of RDS, emphasizing the importance of targeted training to address human performance limitations. It further highlights the need for system improvements to address challenges like latency and the limited haptic feedback replaced by visual feedback, which affect the RDs' perception and vehicle control.