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 Validation of an Assistance System for Merging Maneuvers in Highways in Real Driving Conditions

2019 ◽  
Vol 18 (6) ◽  
pp. 525-531
Author(s):  
S. Sanchez-Mateo ◽  
E. Perez-Moreno ◽  
F. Jimenez ◽  
F. Serradilla ◽  
A. Cruz Ruiz ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Human Error ◽  
Traffic Accidents ◽  
Tracking System ◽  
Assistance System ◽  
Highway Traffic ◽  
Driver Assistance ◽  
National Highway Traffic Safety ◽  
Attentional Load ◽  
Driving Conditions

In the latest study conducted by the National Highway Traffic Safety Administration in 2018, it was published that human error is still considered the major factor in traffic accidents, 94 %, compared with other causes such as vehicles, environment and unknown critical reasons. Some driving scenarios are especially complex, such as highways merging lanes, where the driver obtains information from the environment while making decisions on how to proceed to perform the maneuver smoothly and safely. Ignorance of the intentions of the drivers around him leads to risky situations between them caused by misunderstandings or erroneous assumptions or perceptions. For this reason, Advanced Driver Assistance Systems could provide information to obtain safer maneuvers in these critical environments. In previous works, the behavior of the driver by means of a visual tracking system while merging in a highway was studied, observing a cognitive load in those instants due to the high attentional load that the maneuver requires. For this reason, a driver assistance system for merging situations is proposed. This system uses V2V communications technology and suggests to the driver how to modify his speed in order to perform the merging manoeuver in a safe way considering the available gap and the relative speeds between vehicles. The paper presents the results of the validation of this system for assisting in the merging maneuver. For this purpose, the interface previously designed and validated in terms of usability, has been integrated into an application for a mobile device, located inside the vehicle and tests has been carried out in real driving conditions.

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.

scholarly journals Classification of Inter-Urban Highway Drivers’ Resting Behavior for Advanced Driver-Assistance System Technologies using Vehicle Trajectory Data from Car Navigation Systems

2020 ◽  
Vol 12 (15) ◽  
pp. 5936 ◽  
Author(s):  
Jaeheon Choi ◽  
Kyuil Lee ◽  
Hyunmyung Kim ◽  
Sunghi An ◽  
Daisik Nam
Keyword(s):  
Traffic Safety ◽  
Driver Assistance System ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Statistical Hypothesis ◽  
Navigation Systems ◽  
Trajectory Data ◽  
Driver Assistance ◽  
Resting Behavior ◽  
Car Navigation

Fatigue-related crashes, which are mainly caused by drowsy or distracted driving, account for a significant portion of fatal accidents on highways. Smart vehicle technologies can address this issue of road safety to improve the sustainability of transportation systems. Advanced driver-assistance system (ADAS) can aid drowsy drivers by recommending and guiding them to rest locations. Past research shows a significant correlation between driving distance and driver fatigue, which has been actively studied in the analysis of resting behavior. Previous research efforts have mainly relied on survey methods at specific locations, such as rest areas or toll booths. However, such traditional methods, like field surveys, are expensive and often produce biased results, based on sample location and time. This research develops methods to better estimate travel resting behavior by utilizing a large-scale dataset obtained from car navigation systems, which contain 591,103 vehicle trajectories collected over a period of four months in 2014. We propose an algorithm to statistically categorize drivers according to driving distances and their number of rests. The main algorithm combines a statistical hypothesis test and a random sampling method based on the renowned Monte-Carlo simulation technique. We were able to verify that cumulative travel distance shares a significant relationship with one’s resting decisions. Furthermore, this research identifies the resting behavior pattern of drivers based upon their travel distances. Our methodology can be used by sustainable traffic safety operators to their driver guiding strategies criterion using their own data. Not only will our methodology be able to aid sustainable traffic safety operators in constructing their driver guidance strategies criterion using their own data, but it could also be implemented in actual car navigation systems as a mid-term solution. We expect that ADAS combined with the proposed algorithm will contribute to improving traffic safety and to assisting the sustainability of road systems.

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
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