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Connected autonomous vehicles (CAVs) have fostered the development of intelligent transportation systems that support critical safety information sharing with minimum latency and making driving decisions autonomously. However, the CAV environment is vulnerable to different external and internal attacks. Authorized but malicious entities which provide wrong information impose challenges in preventing internal attacks. An essential requirement for thwarting internal attacks is to identify the trustworthiness of the vehicles. This paper exploits interaction provenance to propose a trust management framework for CAVs that considers both in-vehicle and vehicular network security incidents, supports flexible security policies and ensures privacy. The framework contains an interaction provenance recording and trust management protocol that extracts events from interaction provenance and calculates trustworthiness using fuzzy policies based on the events. Simulation results show that the framework is effective and can be integrated with the CAV stack with minimal computation and communication overhead.
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Classification of Roadway Infrastructure and Collaborative Automated Driving System
The latest developments in vehicle-to-infrastructure (V2I) and vehicle-to-anything (V2X) technologies enable all the entities in the transportation system to communicate and collaborate to optimize transportation safety, mobility, and equity at the system level. On the other hand, the community of researchers and developers is becoming aware of the critical role of roadway infrastructure in realizing automated driving. In particular, intelligent infrastructure systems, which leverage modern sensors, artificial intelligence, and communication capabilities, can provide critical information and control support to connected and/or automated vehicles to fulfill functions that are infeasible for automated vehicles alone due to technical or cost considerations. However, there is limited research on formulating and standardizing the intelligence levels of road infrastructure to facilitate the development, as the SAE automated driving levels have done for automated vehicles. This article proposes a five-level intelligence definition for intelligent roadway infrastructure, namely, connected and automated highway (CAH). The CAH is a subsystem of the more extensive collaborative automated driving system (CADS), along with the connected automated vehicle (CAV) subsystem. Leveraging the intelligence definition of CAH, the intelligence definition for the CADS is also defined. Examples of how the CAH at different levels operates with the CAV in the CADS are also introduced to demonstrate the dynamic allocation of various automated driving tasks between different entities in the CADS.
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- Award ID(s):
- 2222541
- PAR ID:
- 10465948
- Date Published:
- Journal Name:
- SAE International Journal of Connected and Automated Vehicles
- Volume:
- 6
- Issue:
- 4
- ISSN:
- 2574-0741
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.4271/12-06-04-0026
