Traffic Safety Improvement through Evaluation of Driver Behavior An Initial Step Towards Vehicle Assessment of Human Operators Clemson University

Format:

Book

Conference/Event

Author/Creator:

Wang, Chengshi, author.

Contributor:

Alexander, Kim

Wagner, John

Wang, Yue

Conference Name:

WCX SAE World Congress Experience (2023-04-18 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2023

Summary:

In the United States and worldwide, 38,824 and 1.35 million people were killed in vehicle crashes during 2020. These statistics are tragic and indicative of an on-going public health crisis centered on automobiles and other ground transportation solutions. Although the long-term US vehicle fatality rate is slowly declining, it continues to be elevated compared to European countries. The introduction of vehicle safety systems and re-designed roadways has improved survivability and driving environment, but driver behavior has not been fully addressed. A non-confrontational approach is the evaluation of driver behavior using onboard sensors and computer algorithms to determine the vehicle's "mistrust" level of the given operator and the safety of the individual operating the vehicle. This is an inversion of the classic human-machine trust paradigm in which the human evaluates whether the machine can safely operate in an automated fashion. The impetus of the research is the recognition that human error is responsible for over 90% of motor vehicle crashes. In this paper, a novel mistrust algorithm is introduced that considers both human and vehicle performance to continually update the mistrust metric. The mistrust metric is continually calculated and compared to a priori thresholds leading to safety categorization as normal, aggressive, dangerous, or critical. A full nonlinear virtual automotive simulation has been created with advanced driver safety systems on demand and virtual drivers in traffic to demonstrate the concept. A series of seven driving scenarios have been investigated which feature nine adverse operator behaviors. Numerical results show that the proposed mistrust algorithm, with vehicle ADAS system, can enhance occupant safety. The potential of this traffic safety strategy merits consideration as an alternative driving adaptation for at-risk drivers as autonomous vehicle technology continues to emerge

Notes:

Vendor supplied data

Publisher Number:

2023-01-0569

Access Restriction:

Restricted for use by site license