A Probabilistic Framework for Decision-Making in Collision Avoidance Systems

Brännström, Mattias; Sandblom, Fredrik; Hammarstrand, Lars

doi:10.1109/tits.2012.2227474

Cited by 61 publications

(27 citation statements)

References 20 publications

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“…The key factor of system quality is to know the exact present position of a vehicle, and to predict its future position accurately by monitoring the movement of a vehicle for collision avoidance [4] [5].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, in real traffic scenarios, thousands of vehicles operate in the same district at the same time. It is unlikely for a server to fulfill the task of cooperative localization owing to limited computational resources as well as limited network bandwidth [5] [8]. This study addresses this issue by proposing a method that achieves minimal use of wireless network bandwidth and optimal computational cost while not missing true warnings.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Intersection Collision Monitoring Algorithm Based on VANETs and Uncertain Trajectory

Ding¹,

Wu²,

Dong³

et al. 2018

EasyChair Preprints

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Abstract-In order to ensure driving safely, the driving safety assistance system must be able to aware of potential collision accidents in advance, especially significant for the intersection where traffic accidents occur more frequently. Considering that VANETs is one of the most important applications for improving the safety of driving, furthermore, vehicles have an inherent uncertainty of location because the exact position of a moving object is known, with certainty, only at the time of an update on position information. In order to reduce the accident rate at intersection and combine the driving characteristics of vehicles at traffic intersections, a vehicle intersection collision monitoring algorithm based on VANETs and uncertain trajectory is proposed. The algorithm is divided into two categories: uncertain trajectory prediction algorithm and vehicle collision monitoring algorithm. The proposed approach provides approximate answers to the user at the users required level of accuracy while achieving near-optimal communication and computational costs. Finally, extensive experiments were conducted to show the efficiency and efficacy of the proposed approach.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle Intersection Collision Monitoring Algorithm Based on VANETs and Uncertain Trajectory

Ding¹,

Wu²,

Dong³

et al. 2018

EasyChair Preprints

View full text Add to dashboard Cite

show abstract

“…Modern research focuses on determination driver's stress level exploiting either physiological signals or driver's reactions [3][4][5][6][7][8][9]. Particularly, Healey et al [4] investigate which physiological signals are adequate for stress detection.…”

Section: Introductionmentioning

confidence: 99%

“…For that purpose conductivity in combination with heart rate [5,6], facial expressions, eye movement along with speech recognition errors have been utilized [7]. The actuation of gas/brake pedal can be exploited for stress level determination [8,9]. However, Rigas et al [3] propose a comprehensive system for driver's stress level detection that utilizes both physiological signals and complementary data, such as past observations of driving behavior.…”

Section: Introductionmentioning

confidence: 99%

Stress-oriented driver assistance system for electric vehicles

Athanasiou

Tsotoulidis

Mitronikas

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Stress is physiological and physical reaction that appears in highly demanding situations and affects human's perception and reaction capability. Occurrence of stress events within highly dynamic road environment could lead to life-threatening situation. With the perspective of safety and comfort driving provision to anxious drivers, in this paper a stress-oriented Driver Assistance System (DAS) is proposed. The DAS deployed on Electric Vehicle. This novel DAS customizes driving command signal in respect to road context, when stress is detected. The effectiveness of this novel DAS is verified by simulation in MATLAB/SIMULINK environment.

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“…센서들의 정보를 받아 들이는 부분(sensor fusion), 이 정보를 이용해 충돌의 위험 도를 판단하는 부분(decision making), 그리고 판단 결과를 가지고 실제로 차량을 통제하는 부분(actuator)이다 [3].…”

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Prediction of Centerlane Violation for vehicle in opposite direction using Fuzzy Logic and Interacting Multiple Model

Kim¹,

Choi²,

An³

et al. 2013

Journal of Korean Institute of Intelligent Systems

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For intelligent vehicle technology, it is very important to recognize the states of around vehicles and assess the collision risk for safety driving of the vehicle. Specifically, it is very fatal the collision with the vehicle coming from opposite direction. In this paper, a centerlane violation prediction method is proposed. Only radar signal based prediction makes lots of false alarm cause of measurement noise and the false alarm can make more danger situation than the non-prediction situation. We proposed the novel prediction method using IMM algorithm and fuzzy logic to increase accuracy and get rid of false positive. Fuzzy logic adjusts the radar signal and the IMM algorithm appropriately. It is verified by the computer simulation that shows stable prediction result and fewer number of false alarm.

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A Probabilistic Framework for Decision-Making in Collision Avoidance Systems

Cited by 61 publications

References 20 publications

Vehicle Intersection Collision Monitoring Algorithm Based on VANETs and Uncertain Trajectory

Vehicle Intersection Collision Monitoring Algorithm Based on VANETs and Uncertain Trajectory

Stress-oriented driver assistance system for electric vehicles

Prediction of Centerlane Violation for vehicle in opposite direction using Fuzzy Logic and Interacting Multiple Model

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