A Collision Warning System for rear-end collision: a driving simulator study

Bella, Francesco; Russo, R.

doi:10.1016/j.sbspro.2011.08.075

Cited by 108 publications

(37 citation statements)

References 17 publications

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“…Bella [7] Francesco and Russo Roberta, presented a paper on, 'A Collision Warning System for rear-end collision: a driving simulator study', studying the driver's conduct during car-following tactics.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Efficient Automotive Collision Avoidance System for Indian Traffic Conditions

.¹

2016

IJRET

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The execution of a wide-ranging accident cautioning system is being visualised by ACAS Program, which is proficient in perceiving and cautioning the driver of potential hazard conditions forward, on the side, and rear sections of the vehicle. The structure would use: (a) long array sensor or visual devices to identify probable hazards in front of the vehicle, (2) short range sensors to caution the driver of nearby objects while changing traffic lanes or backing up, and (3) a track exposure arrangement to alert the motorist when the vehicle deviates from the intended traffic lane. Accident Prevention organisms, as a consequent step to collision mitigation, are one of the Great challenges in the area of active safety for road vehicles. The task of a collision avoidance system is to track objects of potential collision threat and govern any action to evade or diminish a crash. The main focus is how to make decisions based on ambiguous evaluations and in the presence of numerous hurdles. The first step in CA (Collision Avoidance) systems for automotive applications is adaptive cruise control (ACC). The ability of the car to protect its passengers is sometimes called crashworthiness. Generally, a CM (Collision Mitigation) system tries to reduce the severity of the accident as much as possible under some constraints. The situation may be observed with detector sensors, laser radar, vision sensors, ultrasonic sensors, GPS sensors and inter-vehicle communication. FCM (Forward Collision Mitigation) systems mainly try to avoid or mitigate frontal collisions. The CA decision is based on the current position estimate of the host vehicle and those of other objects. The focus is towards the method for determining the threat of a collision given that the state of other objects is known. Two collision mitigation by braking systems are considered for study. One system uses the probability of collision, to decide when to perform braking interventions. The other system has a multiple obstacle decision. The scenarios that are calculated in this segment are primarily those where a CMbB has a large potential of significantly reducing the collision speed. Therefore, the test results of main interest is those between 0 and 70 km/h also for simulations. Hence, this ACAS Program detects the potential hazards, warns the driver and takes action to avoid or mitigate a collision.

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Section: Literature Reviewmentioning

confidence: 99%

“…When this threshold is surpassed, an alarm indicates the driver to follow precautionary action to avoid a crash. [7] When the CMBS is on, ahead vehicle is constantly scanned by the radar sensor. If a possible collision with the vehicle ahead of you is sensed by the system, it responds with an audible alarm and a visual alarm.…”

Section: Fig-12mentioning

confidence: 99%

An Efficient Automotive Collision Avoidance System for Indian Traffic Conditions

.¹

2016

IJRET

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“…FCWS are based on sensors which continuously monitor certain parameters such as the relative speed, lateral position and distance between two vehicles. Although a wide variety of algorithms have been designed to calculate the moment for triggering the warning, most of them are based either on the time to collision between the obstacle and the following vehicle or on the minimum distance required to stop the vehicle safely [5]. When a certain threshold value is reached, predicting a potential risk of collision, the system provides a warning signal (e.g.…”

Section: The Utility Of Fcwsmentioning

confidence: 99%

Effectiveness of Forward Collision Warning Systems: A contribution from the cognitive analysis combining behavioural and electrophysiological measurements

Bueno¹,

Fabrigoule²,

Fort³

2013

Driver Adaptation to Information and Assistance Systems

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This chapter discusses Forward Collision Warning Systems (FCWS), describing the characteristics and the functions of some of the systems currently on the market and presents an overview of behavioural studies evaluating the effectiveness of these systems on road and in simulators. Results are presented from recent studies using electroencephalography and the associated Event Related Potentials allowing, through the analysis of brain activity, a more indepth understanding of the nature of the cognitive processes in the context of FCWS. These studies address three important questions: 1. Are FCWS as effective as they are expected to be when drivers are distracted? 2. What are the consequences of driving with a system that is not completely reliable? 3. Is there any behavioural adaptation to the FCWS over their use in time? Are the consequences of this adaptation positive or negative? The chapter shows that FCWS provides potential benefits for road safety, but certain factors such as the attentional state of drivers but the reliability level of the system can mitigate its effectiveness.

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“…Results indicated that drivers who do not foresee the sudden brake of the lead vehicle always cannot react in time. Based on a driving simulator experiment, Bella et al [40] analyzed the driving behaviour during a car-following situation and developed a new collision warning algorithm which can reflect the real risk perception by drivers.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of Rear-End Collision Risk Based on Car-Following Driving Simulation Experiments

Xue

Yan

et al. 2018

Discrete Dynamics in Nature and Society

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Rear-end collisions are one of the most common types of accidents, and the importance of examining rear-end collisions has been demonstrated by numerous accidents analysis researches. Over the past decades, lots of models have been built to describe driving behaviour during car following to better understand the cause of collisions. However, it is necessary to consider individual difference in car-following modelling while it seems to be ignored in most of previous models. In this study, a rear-end collision avoidance behaviour model considering drivers' individual differences was developed based on a common deceleration pattern extracted from driving behaviour data, which were collected in a car-following driving simulation experiment. Parameters of variables in the model were calibrated by liner regression and Monte Carlo method was adopted in model simulation for uncertainty analysis. Simulation results confirmed the effectiveness of this model by comparing them to the experiment data and the influence of driving speed and headway distance on the rear-end collision risk was indicated as well. The thresholds for driving speed and headway distance were 18 m/s and 15 m, respectively. An obvious increase of collision risk was observed according to the simulation results.Hindawi

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A Collision Warning System for rear-end collision: a driving simulator study

Cited by 108 publications

References 17 publications

An Efficient Automotive Collision Avoidance System for Indian Traffic Conditions

An Efficient Automotive Collision Avoidance System for Indian Traffic Conditions

Effectiveness of Forward Collision Warning Systems: A contribution from the cognitive analysis combining behavioural and electrophysiological measurements

Uncertainty Analysis of Rear-End Collision Risk Based on Car-Following Driving Simulation Experiments

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