Using Crash Databases to Predict Effectiveness of New Autonomous Vehicle Maneuvers for Lane-Departure Injury Reduction
(2021) In IEEE Transactions on Intelligent Transportation Systems 22(6). p.3479-3490- Abstract
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... (More)
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.
(Less)
- author
- Olofsson, Bjorn LU and Nielsen, Lars
- organization
- publishing date
- 2021-06-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Accidents, active safety, Databases, extended yaw control, Injuries, Optimization, Risk analysis, Roads, Safety, Vehicle crash testing, vehicle stability control, vehicle-braking strategies.
- in
- IEEE Transactions on Intelligent Transportation Systems
- volume
- 22
- issue
- 6
- pages
- 12 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85083464559
- ISSN
- 1524-9050
- DOI
- 10.1109/TITS.2020.2983553
- project
- ELLIIT LU P11: Online Optimization and Control towards Autonomous Vehicle Maneuvering
- RobotLab LTH
- language
- English
- LU publication?
- yes
- id
- 72e5058c-0446-4b81-93c2-4d1be060f33b
- date added to LUP
- 2020-05-08 15:04:31
- date last changed
- 2023-04-24 21:08:44
@article{72e5058c-0446-4b81-93c2-4d1be060f33b,
abstract = {{<p>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.</p>}},
author = {{Olofsson, Bjorn and Nielsen, Lars}},
issn = {{1524-9050}},
keywords = {{Accidents; active safety; Databases; extended yaw control; Injuries; Optimization; Risk analysis; Roads; Safety; Vehicle crash testing; vehicle stability control; vehicle-braking strategies.}},
language = {{eng}},
month = {{06}},
number = {{6}},
pages = {{3479--3490}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Intelligent Transportation Systems}},
title = {{Using Crash Databases to Predict Effectiveness of New Autonomous Vehicle Maneuvers for Lane-Departure Injury Reduction}},
url = {{http://dx.doi.org/10.1109/TITS.2020.2983553}},
doi = {{10.1109/TITS.2020.2983553}},
volume = {{22}},
year = {{2021}},
}