scholarly journals Development of Improvements to Driver Assistance System “EyeSight” for Reduction of Traffic Accidents

2017 ◽  
Vol 25 (4) ◽  
pp. 383-389 ◽  
Author(s):  
Shigeo USUI ◽  
Naoki NOMURA ◽  
Hikaru KUMAGAI ◽  
Hiroshi SEKINE
Keyword(s):  
Traffic Accidents ◽  
Driver Assistance System ◽  
Assistance System ◽  
Driver Assistance

scholarly journals Smart Driver Assistance System for Accident Forestalling

2019 ◽  
Vol 9 (1S) ◽  
pp. 111-113
Keyword(s):  
Traffic Accidents ◽  
Driver Assistance System ◽  
Focal Point ◽  
Family Members ◽  
Modern Technology ◽  
Assistance System ◽  
Driver Assistance ◽  
Alert Message ◽  
The Status

Traffic accidents that happenedaround the worldare increasing a lot. If modern technology is incorporated within vehicle to find the status of the driving person at regular intervals and assist driver about sign boards so that the driver would not lose focal point. The sign board is monitored by using webcam and the text from the image is converted into audio and it directs the driving person. This system observes the heartbeat, sense drowsiness of driver and checks whether the driver has consumed alcohol. If any disaster is noticed, system sends an alert message including the location to the service, sickbay and the person’s family members and if there is no serious risk, then the aware message can be ended by the driver in order to avoid wasting the valuable time.

Risk Predictive Driver Assistance System for Collision Avoidance in Intersection Right Turns

2018 ◽  
Vol 30 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Yohei Fujinami ◽  
Pongsathorn Raksincharoensak ◽  
Dirk Ulbricht ◽  
Rolf Adomat ◽  
◽  
...  
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Driver Assistance System ◽  
Blind Spot ◽  
Assistance System ◽  
Driver Assistance ◽  
Emergency Braking ◽  
Safety Function ◽  
Vehicle Velocity ◽  
Right Turns

Most traffic accidents that result in injuries or fatalities occur in intersections. In Japan, where cars drive on the left, most of such accidents involve cars that are turning right. This situation serves as the basis of the development of our Advanced Driver Assistance System (ADAS) for intersection right turns. This research focuses on the scenario in which an object darts out from the blind spot created by heavy oncoming traffic as a vehicle is making an intersection right turn. When this happens, even if the driver brakes as hard as possible or an active safety function such as the Autonomous Emergency Braking System (AEBS) applies the brakes, the natural limits of physical friction may make it impossible to avoid a collision. To improve traffic safety given the limited potential of physical friction, this research seeks to develop a risk-predictive right-turn assistance system. The system predicts potential oncoming objects and reduces the vehicle velocity in advance. Blind corners can be detected by on-board sensors without requiring information from surrounding infrastructure. This paper presents a right-turn assistance system that avoids conflict with the AEBS in emergencies by decelerating the ego vehicle to a safe velocity.

scholarly journals Experimental Validation on Intersection Turning Trajectory Prediction Method for Advanced Driver Assistance System Based on Triclothoidal Curve

2021 ◽  
Vol 11 (13) ◽  
pp. 5900
Author(s):  
Yohei Fujinami ◽  
Pongsathorn Raksincharoensak ◽  
Shunsaku Arita ◽  
Rei Kato
Keyword(s):  
Traffic Accidents ◽  
Computational Cost ◽  
Prediction Method ◽  
Smooth Curve ◽  
Assistance System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Trajectory Prediction ◽  
Left Turn ◽  
Left Hand

Advanced driver assistance systems (ADAS) for crash avoidance, when making a right-turn in left-hand traffic or left-turn in right-hand traffic, are expected to further reduce the number of traffic accidents caused by automobiles. Accurate future trajectory prediction of an ego vehicle for risk prediction is important to activate the assistance system correctly. Our objectives are to propose a trajectory prediction method for ADAS for safe intersection turnings and to evaluate the effectiveness of the proposed prediction method. Our proposed curve generation method is capable of generating a smooth curve without discontinuities in the curvature. By incorporating the curve generation method into the vehicle trajectory prediction, the proposed method could simulate the actual driving path of human drivers at a low computational cost. The curve would be required to define positions, angles, and curvatures at its initial and terminal points. Driving experiments conducted at real city traffic intersections proved that the proposed method could predict the trajectory with a high degree of accuracy for various shapes and sizes of the intersections. This paper also describes a method to determine the terminal conditions of the curve generation method from intersection features. We set a hypothesis where the conditions can be defined individually from intersection geometry. From the hypothesis, a formula to determine the parameter was derived empirically from the driving experiments. Public road driving experiments indicated that the parameters for the trajectory prediction could be appropriately estimated by the obtained empirical formula.

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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