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Dieses Kapitel befasst sich mit dem Distributed ODD Attribute Value Awareness (DOVA) Framework, einem revolutionären Ansatz zur Verbesserung der operativen Fähigkeiten automatisierter Fahrsysteme (ADS). Durch die Nutzung von Sensorinfrastruktur und kommerziellen Dienstleistungen ermöglicht das DOVA-Rahmenwerk ADS den Zugriff auf wichtige ODD-Attributinformationen, die sie möglicherweise nicht unabhängig messen können. Dieses Kapitel untersucht die praktischen Anwendungen des DOVA-Rahmenwerks und hebt seine Rolle bei der Verbesserung des Verkehrsmanagements, der Straßensicherheit und der Gesamteffizienz von ADS hervor. Außerdem wird die Bedeutung von Datenqualität, Zuverlässigkeit und Cybersicherheit für die erfolgreiche Umsetzung des DOVA-Rahmenwerks diskutiert. Das Kapitel schließt mit einer Beschreibung der zahlreichen Möglichkeiten, die das DOVA-Rahmenwerk für die Straßenbetreiber bietet, darunter verbessertes Störungs- und Ereignismanagement, maßgeschneidertes Verkehrsmanagement und verbesserte digitale Infrastruktur. Die in diesem Kapitel vorgestellten Forschungsarbeiten sind Teil des Projekts Verkehrsmanagement für vernetztes und automatisiertes Fahren (TM4CAD), das von der CEDR Transnational Research Programme Call 2020 gefördert wird.
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Abstract
The paper presents the concept of Distributed Operational Design Domain (ODD) attribute Value Awareness (DOVA) developed by TM4CAD (Traffic Management for Connected and Automated Driving), a CEDR-funded project involving MAPtm (coordinator), Traficon, Transport & Mobility Leuven, and WMG, University of Warwick, as well as Hironao Kawashima from Japan and Steven Shladover from USA as experts. TM4CAD explored the role of infrastructure systems and connected vehicle data exchange in creating ODD (Operational Design Domain) attribute value awareness for Automated Driving Systems (ADS), so that each ADS can be forewarned before it encounters conditions in which it will not be capable of operating, i.e. ODD departure. TM4CAD proposed various approaches for providing distributed ODD attribute value information and its importance, acquisition principles, and corresponding data quality requirements. It also discussed the basic mechanisms of ODD management via specific use cases, which build on the interaction between traffic management systems and the ADS, and highlighted the need for stronger interactions between national road authorities and ADS developers.
1 Distributed ODD Attribute Value Awareness
1.1 The DOVA Framework
The need to monitor or be aware of each ODD attribute puts an additional overhead on the Automated Driving System (ADS) to be able to measure each ODD attribute. However, measuring each ODD attribute may not be practically feasible from a cost and engineering perspective. However, ODD awareness is key to ensuring safe operation of the ADS. In order to overcome this challenge, TM4CAD has introduced the concept of Distributed ODD attribute Value Awareness (DOVA) framework [1].
The DOVA framework enables the ADS to benefit from off-board sensing infrastructure to become aware of ODD attribute values which it may not be able to measure or sense by itself. For example, an ADS may not be able to detect the severity of a visibility impairment from a fog bank that it is approaching. It may be able to receive such information from a roadside weather station which can provide this information through over the air communication with the ADS. This enables the ADS to have awareness of this current operating condition and compare it with its designed ODD to establish if the ADS is either inside or outside its ODD. While information for some of the ODD attributes could be available via infrastructure, there may potentially be commercial services which can augment ODD information for the ADS [2].
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From a road operator perspective, it is important to establish what type of ODD attribute information should be provided via infrastructure and its corresponding quality to enable safe deployment of ADS. It is also important to consider the needs of the road operators and traffic managers to be aware of any ADS approaching the end of their ODD and/or being in a transitional or minimal risk state [2].
The operation of the DOVA framework in practice is illustrated in Fig. 1. The ODD attribute information (or from the road operator perspective, local condition attribute information) sharing plays a major role in influencing the driving behaviour of the ADS-operated vehicle depending on its technical capabilities and the rules of the road. The traffic management operations affect the rules of the road (i.e., the expected behaviour) as well as the status of the ODD / local condition attributes sensed by the vehicle, the road operators’ and other stakeholders’ monitoring and other data acquisition systems providing the attribute information to the ADS-operated vehicles and other road users.
Fig. 1.
Distributed ODD attribute Value Awareness (DOVA) Framework [1].
We found out that any external ODD or local condition information from infrastructure can bring redundancy, i.e., backup for the automated driving systems. The ODD attributed related external information can likely always be regarded at least as “nice-to-have” as it can extend the electronic horizon of the vehicle beyond the range of the vehicle sensors.
In the future, regulations could specify that the road operator or traffic manager must provide specific information attributes to the ADS. Some of these information attributes are necessary for ADS operation while others are relevant to managing the traffic.
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Basically, the ODD/local conditions attributes that the AV industry indicated being priority for them, were also considered priority for the traffic managers and road maintenance operators in most of the cases in the scenarios of traffic jam, adverse weather area and road works.
A primary open issue is the basic one of trust. Vehicle manufacturers and ADS developers will use the data as a basis for automated vehicle operation only if they can trust the data to be correct, reliable, and secure. Much work needs to be done to improve the quality of the data, the reliability of the data and its exchange as well as the cybersecurity of the DOVA process to the level satisfying the liability-related requirements of the automated vehicle industry.
In the discussions with the road operators it has become clear that the required major improvements with regard to some quality indicators such as network coverage, location accuracy and timeliness can not realistically be achieved by the road operators on their own but only via information shared by the connected and automated vehicles. This would be a clear future win-win situation where ADS fleet operators achieve trustful ODD attribute awareness for their ADS in use while the road operators have very good awareness of the local conditions on their road networks.
2 Opportunities for the Road Operators Brought by DOVA
The opportunities are described below for selected core business areas. Those for the other core business areas are only shown later in Table 1. Note that the opportunities build on the availability of data from automated vehicles utilising the DOVA framework on e.g. the ODD unavailability, carrying out of MRMs, etc. The chapter assumes a much broader information provision from the vehicle side than what is contained in the DOVA framework itself. It is likely that the road authorities and operators expect that in return for the ODD-related local condition information provided to the vehicles and fleet operators these would also provide information essential to the road authorities and operators [3].
Table 1.
Road operator opportunities in the other business areas not described in detail.
Core business area
Opportunities
Traffic management and control
Tailored traffic management, merging of traffic and fleet management, new methods due to better compliance, incident prediction/prevention
Road maintenance
Automated maintenance vehicles, real-time digital twins, quicker maintenance operations through better predictability
Winter maintenance
Automated maintenance vehicles, real-time digital twins, quicker maintenance operations through better predictability
Traffic information services
Higher quality data & information via DOVA feedback loop
Enforcement
Better enforcement of road works and maintenance operations by contractors
Road user charging
Charging granularity can be improved
New roads planning and building
More detailed digital twins for BIM (Building Information Modelling
Road works planning/management
More efficient road works management due to meeting DOVA needs
Heavy maintenance planning
More detailed knowledge for AIM (Asset Information Modelling)
New business
New tasks & roles resulting in new services, involvement in ODD & DOVA management, limiting remote supervision
2.1 Physical Road Infrastructure
Feedback from the automated vehicles concerning the issues related to the ADS engagement enable easier real time analysis of physical infrastructure driveability for highly automated vehicles. This also provides data on the frequency of different types of issues in various parts of the network.
If the AV fleet operators are willing the share the location and even in some cases the reasons for the issues with the road operator or traffic management centre, the latter can identify the problems related to their responsibility and find solutions to remove these problems. If the solutions are economically and otherwise feasible, the implementation of the solutions can take place.
The data from the automated vehicles on any problems related to the physical infrastructure will provide a new and important data source for the asset management processes of the road operator. This can relate to all physical infrastructure related ODD attributes such as for instance quality of pavement marking and the load-bearing capacity of roadway or bridge structures [2].
2.2 Digital Infrastructure
The DOVA framework and vehicle originated data will also provide high quality tools for digital road operation. The setting up of the DOVA framework already will indirectly provide improved quality data for all other digital road operator services as the local condition data required by the ADS has higher quality requirements than the conventional services. Thereby the benefits to the existing services will also likely increase [2].
The improved and larger sets of data provide also a good basis for the use of AI (Artificial Intelligence) resulting in improved core business services utilising AI-enhanced digital infrastructure [3].
The data in the DOVA and provided in its feedback loop provide also an excellent basis for developing and enhancing the digital infrastructure asset management and its processes. This asset management would likely target at least the digital infrastructure oriented ODD data source such as the availability of GNSS positioning and its differential correction signals or I2V/V2I communications [2].
2.3 Incident and Event Management
The sensing by automated vehicles may well be the first source of information about an incident on the road. It could also be the first source to detect the effects of an event on or nearby the road resulting in a sudden end of a stopped or slow queue on the road [3].
The thereby improved location accuracy and timeliness of detection will provide quicker and more effective incident and event management services. In addition to location accuracy and timeliness, the automated driving system originated data will likely provide improved event coverage (proportion of incidents or events detected and reacted to) and road network coverage. The coverage will naturally only improve on roads within the ODD of the ADS systems in question [3].
In addition to the benefits related to incident and event impact detection, the ADS supported by DOVA are expected to react better to incident management and information measures complying more uniformly with them compared to human drivers. This will likely reduce the risks of secondary accidents and lessen the congestion caused by incidents and events and thereby also the journey time and harmful emission impacts [3].
2.4 Traffic Management and Control
The DOVA provides a possibility for tailored traffic management for different automated driving use cases and scenarios. This also will require a close cooperation and interaction between traffic managers and ADS fleet operators. This could lead to different types of ODD management use cases where traffic management measures and adapted rules of the road could control, maintain or eliminate the use of ADS on a road section ensuring road safety at the same time [3].
This merging of traffic and fleet management could be especially useful for managing hazardous or XXL goods transports (XXL here means goods with dimensions exceeding the ones accepted by regulations) [3].
The ADS will also comply better with traffic management measures than human drivers. Higher compliance rate with regard to traffic management measures means that new methods of traffic management are required. Conventional traffic management measures for instance for rerouting result in only a moderate part of the vehicle drivers to divert accordingly, which in most cases is useful as high diversion rates could cause major congestion issues on the detour [4]. With ADS and their high compliance rate traffic managers need to direct the optimal portions of vehicles to specific detours based on the capacity and other characteristics of the detour (e.g. no heavy goods vehicles should be directed through small village communities).
If available, improved and more comprehensive real-time data in terms of floating vehicle data can be used in actual incident prediction and prevention via traffic management tools utilising AI. This can transform the current reactive traffic management to proactive traffic management where instead of reacting efficiently to incidents to mitigate their impacts and to remove them as quickly as possible traffic management focuses on preventing the incidents from occurring at all. This could be possible by detecting the first symptoms of a likely incident and start preventive measures accordingly in good time.
3 Conclusions
The opportunities for other business areas are compiled in Table 1. It is evident that the vehicle-oriented data provided related to the operation of the DOVA framework brings a lot of opportunities for the road operators. It is perhaps surprising that opportunities were identified even in core business areas quite remote for traffic management and enabling highly automated driving.
The research was part of the Traffic Management for Connected and Automated Driving (TM4CAD) project funded by CEDR’s Transnational Research Programme Call 2020 Impact of CAD on Safe Smart Roads (https://cedr.eu/peb-call-2020-impact-of-cad-on-safe-smart-roads). Note that further information and access to the documents is available at the project website https://tm4cad.project.cedr.eu/.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
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Khastgir, S., Shladover, S., Vreeswijk, J., Kulmala, R., Wijbenga, A.: Report on ODD-ISAD architecture and NRA governance structure to ensure ODD compatibility. TM4CAD Deliverable D2.1. Version 2.0. March (2023)
2.
Kulmala, R., et al.: Information exchange between traffic management centres and automated vehicles – information needs, quality and governance. TM4CAD Deliverable 3.1, Version 2.0. (2023)
3.
Vreeswijk, J., et al.: Implementation aspects of Distributed ODD attribute value awareness. Traffic Management for Connected and Automated Driving (TM4CAD) Deliverable D4.1. CEDR’s Transnational Research Programme Call 2020 Impact of CAD on Safe Smart Roads (2023)
4.
EU EIP: Reference Handbook for harmonized ITS core service deployment in Europe. European ITS Platform EU EIP 12/10/2021 (2021)
