Acclimatizing the Operational Design Domain for Autonomous Driving Systems

Sun, Chen; Deng, Zejian; Chu, Wenbo; Li, Shen; Cao, Dongpu

doi:10.1109/mits.2021.3070651

Cited by 29 publications

(10 citation statements)

References 46 publications

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“…The ODD was defined by the National Highway Traffic Safety Administration in 2017 [ 7 ]. This is the key to ensuring safety in the functional boundaries of an ADS, that is, an autonomous vehicle (AV) [ 8 , 9 ]. It is the domain under which an ADS, can operate properly [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving

Mishra

Kim

Cha

et al. 2021

Sensors

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An authorized traffic controller (ATC) has the highest priority for direct road traffic. In some irregular situations, the ATC supersedes other traffic control. Human drivers indigenously understand such situations and tend to follow the ATC; however, an autonomous vehicle (AV) can become confused in such circumstances. Therefore, autonomous driving (AD) crucially requires a human-level understanding of situation-aware traffic gesture recognition. In AVs, vision-based recognition is particularly desirable because of its suitability; however, such recognition systems have various bottlenecks, such as failing to recognize other humans on the road, identifying a variety of ATCs, and gloves in the hands of ATCs. We propose a situation-aware traffic control hand-gesture recognition system, which includes ATC detection and gesture recognition. Three-dimensional (3D) hand model-based gesture recognition is used to mitigate the problem associated with gloves. Our database contains separate training and test videos of approximately 60 min length, captured at a frame rate of 24 frames per second. It has 35,291 different frames that belong to traffic control hand gestures. Our approach correctly recognized traffic control hand gestures; therefore, the proposed system can be considered as an extension of the operational domain of the AV.

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Section: Methodsmentioning

confidence: 99%

Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving

Mishra

Kim

Cha

et al. 2021

Sensors

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“…DTs have been proposed in the operational design domain (ODD) analysis of such systems for security validation in autonomous systems. Sun et al [46] proposed an architecture design to improve the ODD of autonomous vehicle systems. In this way, it is possible to capture the operational constraints of the generic components of the road environment, such as road type, traffic volume, and weather conditions.…”

Section: A Sensing Consolidation and Scenario Simulationmentioning

confidence: 99%

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Martínez,

Campelo,

Ribeiro

2024

Preprint

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“…Simulating AVs in a virtual world is a powerful technique but is challenging due to the large variety and continuum of scenes that occur in naturalistic driving. To overcome this hurdle, several researchers have taken the scenario based approach to quantify risk for AVs [19,20]. In this approach, the environment is parameterized (e.g., weather = clear, raining, cloudy, road type = highway, rural, urban).…”

Section: A Literaturementioning

confidence: 99%

Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips

Anih

Kolekar²,

Dargahi

et al. 2023

IEEE Access

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The commercial adoption of Autonomous Vehicles (AVs) and the positive impact they are expected to have on traffic safety depends on appropriate insurance products due to the high potential losses. A significant proportion of these losses are expected to occur from the out-of-distribution risks which arise from situations outside the AV's training experience. Traditional vehicle insurance products (for humandriven vehicles) rely on large data sets of drivers' background and historical incidents. However, the lack of such datasets for AVs makes it imperative to exploit the ability to deploy AVs in simulated environments. In this paper, the data collected by deploying Autonomous Driving Systems (ADSs) in simulated environments is used to develop models to answer two questions: (1) how risky a road section is for an AV to drive? and (2) how does the risk profile vary with different (SAE levels) of ADSs? A simulation pipeline was built on the CARLA (Car Learning to Act): an open-source simulator for autonomous driving research). The environment was specified using parameters such as weather, lighting, traffic density, traffic flow, no. of lanes, etc. A metric -risk factor was defined as a combination of harsh accelerations/braking, inverse Time to Collision, and inverse Time Headway to capture the crashes and near crashes. To assess the difference between ADSs, two ADSs: OpenPilot (Level 2/3) and Pylot (Level 4) were implemented in the simulator. The results (from data and model predictions) show that the trends in the relation between the environment features and risk factor for an AV are similar to those observed for human drivers (e.g., risk increases with traffic flow). The models also showed that junctions were a risk hot-spot for both ADSs. The feature importance of the model revealed that the Level 2/3 ADS is more sensitive to no. of lanes and the Level 4 ADS is sensitive to traffic flow. Such differences in feature importance provide valuable insights into the risk characteristics of different ADSs. In the future, this base model will be extended to include other features (other than the environment), e.g., take over requests, and also address the deficiencies of the current simulation data in terms of insensitivity to weather and lighting.

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Acclimatizing the Operational Design Domain for Autonomous Driving Systems

Cited by 29 publications

References 46 publications

Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving

Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips

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