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Systems for Autonomy in Fail-operational Environments (SAFE)

Systems for Autonomy in Fail-operational Environments (SAFE)
故障操作环境中的自主系统 (SAFE)
批准号:
10058962
负责人:
金额:
$157.03万
依托单位:
依托单位国家:
英国
项目类别:
BEIS-Funded Programmes
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

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中文摘要
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英文摘要
The safe deployment of autonomous and remotely operated vehicles is only possible with mature and fault-tolerant vehicle control systems where any kind of vehicle failure is taken into consideration, including overriding autonomous driving software in the event of significant computer system failures. These control systems, also called drive-by-wire (DbW) systems, are not yet commercially available to the vast majority of vehicle manufacturers, thereby severely limiting the potential roll-out of autonomous vehicles across all markets.The SAFE (Systems for Autonomy in Fail-Operational Environments) project will develop and test technologies applicable for both NUIC (No User In Charge) and UIC (User in Charge) vehicle platforms integrating novel safety systems and subsystems capable of achieving SAE Level 4 (L4) autonomy within a wide range of Operational Design Domains (ODDs).StreetDrone will collaborate with two innovative SME tier 1 suppliers, Chassis Autonomy and Alcon, bringing together an overall fail-operational architecture integrating mission critical steering and braking sub-systems. These are safety critical vehicle systems that ensure lateral and longitudinal control of the vehicle at all times, even in the event of a single point fault of failure. A key part of developing the wider supply chain for the CAV sector within the UK is providing safe and secure vehicle hardware for all other downstream systems to be developed and tested upon; without this enabler in place, organisations working on various elements through the self-driving stack cannot successfully develop and validate their products let alone place them into commercial operation.Together, these three innovative UK-SMEs will work alongside the Centre of Automotive Engineering at University of Surrey, developing a credible solution, tested in simulations of multiple real world contexts, and proven on the track by end of 2024, and ready for meeting anticipated Level 4 safety regulations.
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