arXiv subjects

Ole Hans

Publications and source records attributed to Ole Hans.

5 recordsLinked to original sources

arXiv2026

Teleoperation Operational Design Domain based on Minimal Risk Maneuver Capability

This article discusses the concept of an Operational Design Domain (ODD) designed specifically for teleoperated road vehicles. For this purpose, the ODD concept designed for automated driving is adapted for teleoperation. As teleoperation becomes more common in regular traffic, the question arises under which operating conditions such vehicles are able and allowed to drive. Currently, these conditions are selected primarily based on network performance. From a safety perspective, it is difficult to base such a selection on a reliable connection because it is almost impossible to guarantee sufficient reliability. With this in mind, the ODD concept designed for automated driving is adapted for teleoperation: A concept is proposed for basing the ODD for a teleoperation system on the capability of the teleoperated vehicle to perform a minimal risk maneuver using a dedicated system designed solely for this purpose. This concept is then demonstrated using a use case example.

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arXiv2025

Remote Assistance or Remote Driving: The Impact of Operational Design Domains on ADS-Supporting Systems Selection

High level Automated Driving Systems (ADS) can handle many situations, but they still encounter situations where human intervention is required. In systems where a physical driver is present in the vehicle, typically SAE Level 3 systems, this intervention is relatively straightforward and is handled by the in-vehicle driver. However, the complexity increases for Level 4 systems, where, in most cases, no physical driver remains in the vehicle. The two common industry solutions for this challenge are the integration of a remote support system, such as a Remote Driving System (RDS) or Remote Assistance System (RAS). While it is clear that ADS will require one of these systems, it is less clear how the suitability of either system for a particular ADS application should be evaluated. Currently, the selection process often focuses on system architecture as well as its design and integration challenges. Furthermore, since many ADS developers choose to develop remote system solutions in-house, it is advantageous to select the simpler approach to streamline development and integration efforts. While these decision points are certainly relevant, this approach overlooks the most critical factors: the use cases and the complementarity of the ADS and the remote support system within the context of the Operational Design Design Domain (ODD). This paper proposes a structured approach for selecting between RDS and RAS as an ADS support system, based on the defined ODD and use case analysis. To achieve this, the paper applies the PEGASUS framework to systematically describe and analyze the ODD. A structured framework is introduced to evaluate and select the most suitable remote support system for an ADS based on clearly defined criteria.

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arXiv2025

Learning from Disengagements: An Analysis of Safety Driver Interventions during Remote Driving

This study investigates disengagements of Remote Driving Systems (RDS) based on interventions by an in-vehicle Safety Drivers (SD) in real-world Operational Design Domains (ODD) with a focus on Remote Driver (RD) performance during their driving training. Based on an analysis of over 14,000 km on remote driving data, the relationship between the driving experience of 25 RD and the frequency of disengagements is systematically investigated. The results show that the number of SD interventions decreases significantly within the first 400 km of driving experience, which illustrates a clear learning curve of the RD. In addition, the most common causes for 183 disengagements analyzed are identified and categorized, whereby four main scenarios for SD interventions were identified and illustrated. The results emphasize the need for experience-based and targeted training programs aimed at developing basic driving skills early on, thereby increasing the safety, controllability and efficiency of RDS, especially in complex urban environment ODDs.

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arXiv2025

Evaluation of Remote Driver Performance in Urban Environment Operational Design Domains

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.

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arXiv2025

Identification and Classification of Human Performance related Challenges during Remote Driving

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.

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