scholarly journals Driving with Hemianopia VIII: Effects of a Vibro-Tactile Assistance System on Safety and Gaze Behavior in Pedestrian Crossing Situations

Safety ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 18
Author(s):  
Roman Hölzl ◽  
Lorenz Steckhan ◽  
Christian Lehsing ◽  
Steven W. Savage ◽  
Alex R. Bowers
Keyword(s):  
Visual Field ◽  
Driver Assistance System ◽  
Road Traffic ◽  
Assistance System ◽  
Visual Field Defects ◽  
Potential Hazard ◽  
Blind Side ◽  
Gaze Behavior ◽  
Driver Assistance ◽  
Generalized Linear Mixed Effects

People with homonymous visual field defects (HVFDs), the loss of vision in the same half of the visual field in both eyes, are permitted to drive in some jurisdictions. However, the HVFD may cause difficulties in detecting hazards approaching on the side of the field loss (the blind side). An advanced driver assistance system (ADAS) could assist with hazard detection, but little research has been conducted to evaluate the potential benefits of an ADAS for visually impaired drivers. We developed a prototype vibro-tactile assistance system for drivers with HVFDs and conducted a proof-of-concept driving simulation study to evaluate the system. Given that pedestrian accidents are the second most frequent cause of death in road traffic and most of those accidents occur in urban scenarios, we evaluated the potential of the assistance system to improve responses to pedestrian hazards in a city environment. Sixteen participants, of which eight had HVFDs and eight had normal vision, took part. Our analyses evaluated the effects of the driver assistance system, crossing direction, and pedestrian behavior on the safety of pedestrian events and the participant’s gaze behavior at each of the 256 crossing situations. Generalized linear mixed effects models were used to assess binomial outcome variables. Despite the limited sample size, the results suggest that the vibro-tactile directional warnings were effective in directing the drivers’ gaze so that they were looking in the necessary direction before a potential hazard occurred. More time was spent fixating pedestrians on the blind side when the ADAS was engaged, and as a result, the safety of street crossings from the blind side improved. The effect of the ADAS was greater on responses to pedestrians from the blind than the seeing side. With an activated ADAS, crossings from the participants’ blind sides were as safe as crossings from their seeing sides, and it was as safe as the crossings when normally sighted participants were driving. The results suggest that the vibro-tactile ADAS is a promising approach to improve the safety of drivers with HVFD and surrounding traffic.

scholarly journals Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 6985
Author(s):  
Iqram Hussain ◽  
Seo Young ◽  
Se-Jin Park
Keyword(s):  
Resting State ◽  
Driver Assistance System ◽  
Driving Simulator ◽  
Road Traffic ◽  
Mental Workload ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Driver Assistance ◽  
Multi Class Classification ◽  
Prediction Approach

Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological assessment, outside of a highly-equipped clinical setting, requires an ambulatory electroencephalography (EEG) headset. This study aimed to quantify neurological biomarkers during a resting state and two different scenarios of driving states in a virtual driving environment. We investigated the neurological responses of seventeen healthy male drivers. EEG data were measured in an initial resting state, city-roadways driving state, and expressway driving state using a portable EEG headset in a driving simulator. During the experiment, the participants drove while experiencing cognitive workloads due to various driving environments, such as road traffic conditions, lane changes of surrounding vehicles, the speed limit, etc. The power of the beta and gamma bands decreased, and the power of the delta waves, theta, and frontal theta asymmetry increased in the driving state relative to the resting state. Delta-alpha ratio (DAR) and delta-theta ratio (DTR) showed a strong correlation with a resting state, city-roadways driving state, and expressway driving state. Binary machine-learning (ML) classification models showed a near-perfect accuracy between the resting state and driving state. Moderate classification performances were observed between the resting state, city-roadways state, and expressway state in multi-class classification. An EEG-based neurological state prediction approach may be utilized in an advanced driver-assistance system (ADAS).

scholarly journals Driver Assistance System using in-vehicle Traffic Lights and Signs

2018 ◽  
Vol 7 (2.24) ◽  
pp. 527
Author(s):  
Vaibhav Jain ◽  
Tanay . ◽  
Saransh Gangele ◽  
K Kalimuthu
Keyword(s):  
Traffic Congestion ◽  
Driver Assistance System ◽  
Road Traffic ◽  
Obstacle Detection ◽  
Assistance System ◽  
Vehicular Communication ◽  
Driver Assistance ◽  
Traffic Light ◽  
Traffic Lights ◽  
Road Signs

In recent years, with the advancement of vehicular communication, it is possible to detect various road signs and provide traffic light information to the driver inside the vehicle with the application of heads-up display (HUD). It detects road signs, does basic classifications and accordingly directs the driver to slow down or stop the vehicle. The vehicle’s heads-up display keeps the driver focused by providing road warnings, speed limit, traffic signals and some vital navigation information in the driver’s line of sight(LOS). This system has 4 phases, Image recognition, wireless communication, obstacle detection and driver mechanism. This system aims to create a prototype of a smart driver assistance system which provides better road traffic and driver’s safety in countries with high traffic congestion where fully automated vehicles cannot function effectively. This system can be easily implemented in real time scenarios to reduce accidents and enhance the convenience of driving. 

scholarly journals Design and Implementation of Driver Assistance System using Machine Learning

2020 ◽  
Vol 994 ◽  
pp. 012006
Author(s):  
D. S. Bhargava ◽  
N. Shyam ◽  
K. Senthil Kumar ◽  
M. Wasim Raja ◽  
P Sivashankar.
Keyword(s):  
Machine Learning ◽  
Driver Assistance System ◽  
Assistance System ◽  
Driver Assistance ◽  
Design And Implementation

Development of a Driver Assistance System

2003 ◽  
Author(s):  
Shinnosuke Ishida ◽  
Jun Tanaka ◽  
Satoshi Kondo ◽  
Masahito Shingyoji
Keyword(s):  
Driver Assistance System ◽  
Assistance System ◽  
Driver Assistance
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