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.

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

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.

Necessity of Automated Vehicle Control Customization: Experimental Results during Lane Changing

2018 ◽  
Vol 2672 (22) ◽  
pp. 1-9
Author(s):  
Naohisa Hashimoto ◽  
Simon Thompson ◽  
Shin Kato ◽  
Ali Boyali ◽  
Sadayuki Tsugawa
Keyword(s):  
Vehicle Control ◽  
Experimental Results ◽  
Single Type ◽  
Automated Driving ◽  
Lane Changing ◽  
Automated Vehicle ◽  
Vehicle Trajectories ◽  
Significant Difference ◽  
The Subject ◽  
Automated Vehicle Control

This study investigated the necessity of automated vehicle control customization for individual drivers via a lane-changing experiment involving 35 subjects and an automated minivan. The experiment consisted of two automated driving conditions: one in which the subject was unable to override vehicle controls, the other with the option to override when the subject felt it was necessary. The automated vehicle drove at a speed of 40 km/h along three kinds of planned paths for lane changing, generated by Bezier curves; the distance required for lane changing was varied to obtain the preferred path of each subject. Various data obtained during driving, including vehicle trajectories and steering angles produced by subjects were logged. After automated driving, a questionnaire was administered to each subject. The experimental data showed that there was a statistically significant difference between comfort when the vehicle drove along the subject’s preferred path, and when it drove along other paths. The results of the questionnaire indicated that 46% of the subjects preferred the planned path that most closely resembled their own. In addition, quantitative analysis of driving data found that approximately 69% of the subjects preferred an automated driving control that resembled their own. However, it was also observed that certain subjects were open to multiple types of automated vehicle control. The experimental results indicate that drivers will not necessarily accept a single type of automated vehicle control, therefore customization will be necessary to improve acceptance of automated driving.

scholarly journals Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jaehyun Jason So ◽  
Sungho Park ◽  
Jonghwa Kim ◽  
Jejin Park ◽  
Ilsoo Yun
Keyword(s):  
Driving Simulator ◽  
Road Traffic ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle System ◽  
Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

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