Using Crash Databases to Predict Effectiveness of New Autonomous Vehicle Maneuvers for Lane-Departure Injury Reduction
| Autor: | Lars Nielsen, Björn Olofsson |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Risk analysis
Transportteknik och logistik 050210 logistics & transportation Database Relation (database) Computer science Mechanical Engineering 05 social sciences Control (management) Active safety System safety Crash Control Engineering computer.software_genre Computer Science Applications Vehicle engineering Electronic stability control Reglerteknik 0502 economics and business Automotive Engineering computer Transport Systems and Logistics |
| Popis: | Autonomous vehicle functions in safety-critical situations show promise in reducing the risk and saving lives in accidents compared to existing safety systems. Consequently, it is from many perspectives advantageous to be able to quantify the potential benefits of new autonomous systems for vehicle maneuvers at-the-limit of tire friction. Here, to estimate the potential in terms of saved lives and reduced degree of injuries in accidents for new, not yet existing systems, a framework has been developed by combining available historic data, in the form of crash databases, and statistical methods with comparative calculations of vehicle behavior using numerical optimization rather than simulation. The framework performs effectively, it gives interesting insights into the relation between more traditional active yaw control and optimal autonomous lane-keeping control, and it clearly demonstrates the potential of saved lives by using autonomous vehicle maneuvers. Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; ELLIIT Strategic Research Area - Swedish Government |
| Databáze: | OpenAIRE |
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