Human Factors and the Automated Highway System

1992 ◽  
Vol 36 (15) ◽  
pp. 1064-1067 ◽  
Author(s):  
Elizabeth Alicandri ◽  
M. Joseph Moyer
Keyword(s):  
Human Factors ◽  
Control Systems ◽  
Vehicle Control ◽  
Intelligent Vehicle ◽  
Department Of Transportation ◽  
Automated Vehicle ◽  
Automated Highway ◽  
Automated Vehicle Control ◽  
Transportation Program ◽  
Highway System

The Intelligent Vehicle-Highway System (IVHS) is an important and broad ranging Department of Transportation program to reduce congestion and increase safety on the nation's highway system. The Automated Highway System (AHS) represents the full realization of one IVHS subsystem, Automated Vehicle Control Systems. Efforts are underway to define and resolve critical human factors questions related to the AHS. As part of the process, human factors issues will be identified through development of hypothetical AHS scenarios. This requires a generic AHS scenario be presented, and affiliated human factors issues identified.

Human Factor Studies in Evaluation of Automated Highway System Attributes

1997 ◽  
Vol 1573 (1) ◽  
pp. 30-34 ◽  
Author(s):  
Bin Ran ◽  
Shawn Leight ◽  
Seth Johnson ◽  
Wenjing Huang
Keyword(s):  
Human Factor ◽  
Control Process ◽  
Vehicle Control ◽  
Automated Driving ◽  
Training Experience ◽  
Emergency Situations ◽  
Potential System ◽  
Automated Highway ◽  
Automated Vehicle Control ◽  
Highway System

The goal of the Automated Highway System (AHS) is to blend engineering ingenuity and technology to produce a new level of transportation services. Human factors are difficult to integrate with AHS design because they represent a variety of training, experience, skills, and goals. Human factor considerations are essential for AHS design because humans will be involved in automated driving. For instance, drivers may be expected to instruct their vehicles to exit locations, input parameters such as speed and desired headway, or take control in some emergency situations. The tasks that human drivers will be expected to execute have not yet been fully defined. One human factor dilemma that AHS engineers might face is that if human drivers are not allowed to intervene in the vehicle control process during malfunction and emergency situations, they may be trapped in a system with high failure rates. This could result in public distrust and a lack of public will to deploy an AHS. However, if drivers are allowed to take control of their vehicles at will, some may intervene at inappropriate times, causing a potential system failure. A framework has been developed for evaluating human factor concerns for automated vehicle control. These concerns involve basic driving tasks: ( a) detection, ( b) recognition, ( c) situation analysis, ( d) decision making, and ( e) control response. An analytical process to determine the responsibilities of the human driver, vehicle, and AHS infrastructure for these driving tasks is presented.

scholarly journals An Online Evolving Framework for Advancing Reinforcement-Learning based Automated Vehicle Control

2020 ◽  
Vol 53 (2) ◽  
pp. 8118-8123
Author(s):  
Teawon Han ◽  
Subramanya Nageshrao ◽  
Dimitar P. Filev ◽  
Ümit Özgüner
Keyword(s):  
Reinforcement Learning ◽  
Vehicle Control ◽  
Automated Vehicle ◽  
Automated Vehicle Control

Automated vehicle control developments in the PATH program

1991 ◽  
Vol 40 (1) ◽  
pp. 114-130 ◽  
Author(s):  
S.E. Shladover ◽  
C.A. Desoer ◽  
J.K. Hedrick ◽  
M. Tomizuka ◽  
J. Walrand ◽  
...  
Keyword(s):  
Vehicle Control ◽  
Automated Vehicle ◽  
Automated Vehicle Control

scholarly journals Experimental Study on Different Types of Curves for Ride Comfort in Automated Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Naohisa Hashimoto ◽  
Yusuke Takinami ◽  
Makoto Yamamoto
Keyword(s):  
Traffic Accidents ◽  
Field Experiments ◽  
Ride Comfort ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Significance Level ◽  
Automated Vehicle ◽  
Driving Assistance ◽  
Human Factor Research ◽  
Automated Vehicle Control

Vehicle automation is among the best possible solutions for traffic issues, including traffic accidents, traffic jams, and energy consumption. However, the user acceptance of automated vehicles is critical and is affected by riding comfort. In addition, human factors in automated vehicle control should be clear. This study evaluates the effect of different courses on driving comfort in automated vehicles using field experiments with 25 subjects. This study focused on lateral motion, but speed control was not targeted. Further, generating a path for obstacle avoidance and lane keeping, which have several constraining conditions, was also not targeted. Rendering a comfortable path is beneficial for developing an acceptable system as a car developer and for building new curves for automated or driving assistance systems from the perspective of construction. The automated vehicle drove at a speed of 30 km/h on four courses, namely, clothoid, two types of spline curves, and arc, based on the real intersection. Each participant sat on both the driver and passenger seat and answered a questionnaire. The experimental data indicated the clothoid course to be the most comfortable, while the arc was most uncomfortable for a significance level of 1%. These tendencies are applicable to driver and passenger seats, all genders, and experiences and will be beneficial for human factor research in automated vehicle control.

Multiple Fault Diagnosis as Applied to Automated Vehicle Control

1999 ◽  
Author(s):  
Adam S. Howell ◽  
J. Karl Hedrick
Keyword(s):  
Control System ◽  
Diagnostic System ◽  
Vehicle Control ◽  
Longitudinal Control ◽  
Multiple Faults ◽  
Automated Vehicle ◽  
Multiple Fault Diagnosis ◽  
System Components ◽  
Automated Vehicle Control ◽  
Residual Processing

Abstract This paper addresses the problem of detecting multiple faults for the longitudinal control system of an automated vehicle. An existing fault diagnostic system which can isolate all single faults is extended to the diagnosis of multiple faults via improved residual processing in the form of fuzzy logic. The new diagnostic system is shown to correctly detect and isolate all single and multiple faults in a subset of the automated vehicle control system components.

Sign in / Sign up

Export Citation Format

Share Document