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To gain an understanding of the software Application Programming Interface (API) landscape for SAE J3101 [SAEJ3101] devices, the SAE Vehicle Electrical Hardware Security Task Force conducted an analysis of various automotive-relevant crypto APIs. The purpose of this analysis was to identify how well the existing APIs cover the SAE J3101 requirements and to highlight the areas of coverage within these APIs. By examining these APIs, the Task Force aimed to gain insights into the current state of API support for SAE J3101 devices and identify any gaps in coverage that need to be addressed.

The UN R155 regulation is the first automotive cybersecurity regulation and has made security a mandatory approval criterion for new vehicle types. ...These recommendations aim to enhance the comprehensiveness of the security assessment associated with UN R155, fostering a more uniform approach to evaluating cybersecurity in the context of vehicle type approvals.

., driver assistance functions, intrusion detection system, vehicle diagnostics, external device authentication during vehicle diagnostics, vehicle-to-grid and so on). The cybersecurity attacks targeting the global time result in false time, accuracy degradation, and denial of service as stated in IETF RFC 7384 [2].

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This chapter delves into the field of multi-agent collaborative perception (MCP) for autonomous driving: an area that remains unresolved. Current single-agent perception systems suffer from limitations, such as occlusion and sparse sensor observation at a far distance. To address this, three unsettled topics have been identified that demand immediate attention. First, it is crucial to establish normative communication protocols to facilitate seamless information sharing among vehicles. Second, collaboration strategies need to be defined, including identifying the need for specific collaboration projects, determining the collaboration partners, defining the content of collaboration, and establishing the integration mechanism. Finally, collecting sufficient data for MCP model training is vital. This includes capturing diverse modal data and labeling various downstream tasks as accurately as possible.

And as much as there are technical questions regarding network latency, bandwidth, cybersecurity, etc., aspects like human workload, attentiveness, and situational awareness also need to be clarified.

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