Scaling of relative velocity between vehicles

Hoffmann, Errol R.; Mortimer, Rudolf G.

doi:10.1016/0001-4575(96)00005-x

Cited by 89 publications

(28 citation statements)

References 8 publications

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“…At lower velocities, drivers seemed to misestimate the need to brake because of the greater distance between vehicles. Likewise, drivers responded poorly to long headway and high deceleration conditions, which may reflect the relatively poor visual cues available to drivers in these situations (Hoffmann & Mortimer, 1996). Drivers compensated for their delayed accelerator release with a faster transition time from the accelerator to the brake and a faster transition from initial brake application to maximum deceleration.…”

Section: Discussionmentioning

confidence: 99%

Collision Warning Timing, Driver Distraction, and Driver Response to Imminent Rear-End Collisions in a High-Fidelity Driving Simulator

et al. 2002

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Rear-end collisions account for almost 30% of automotive crashes. Rear-end collision avoidance systems (RECASs) may offer a promising approach to help drivers avoid these crashes. Two experiments performed using a high-fidelity motion-based driving simulator examined driver responses to evaluate the efficacy of a RECAS. The first experiment showed that early warnings helped distracted drivers react more quickly---and thereby avoid more collisions---than did late warnings or no warnings. Compared with the no-warning condition, an early RECAS warning reduced the number of collisions by 80.7%. Assuming collision severity is proportional to kinetic energy, the early warning reduced collision severity by 96.5%. In contrast, the late warning reduced collisions by 50.0 % and the corresponding severity by 87.5%. The second experiment showed that RECAS benefits even undistracted drivers. Analysis of the braking process showed that warnings provide a potential safety benefit by reducing the time required for drivers to release the accelerator. Warnings do not, however, speed application of the brake, increase maximum deceleration, or affect mean deceleration. These results provide the basis for a computational model of driver performance that was used to extrapolate the findings and identify the most promising parameter settings. Potential applications of these results include methods for evaluating collision warning systems, algorithm design guidance, and driver performance model input.

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Section: Discussionmentioning

confidence: 99%

Collision Warning Timing, Driver Distraction, and Driver Response to Imminent Rear-End Collisions in a High-Fidelity Driving Simulator

et al. 2002

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“…From this study some information about the first threshold described in the Michaels model was obtained. Indeed, also using results from Hoffmann and Mortimer (1996), they evaluated a perception limit for of about ! (5 times bigger than those given in Michaels).…”

Section: Action Point Paradigmsmentioning

confidence: 99%

Experimental evidence supporting simpler Action Point paradigms for car-following

Pariota

Bifulco

2015

Transportation Research Part F: Traffic Psychology and Behaviou

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ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. IntroductionModelling driving behaviour represents a crucial task for many applications in transportation. Three main areas can particularly benefit from an enhanced knowledge of driving behaviour: accident analysis and prevention, microscopic simulation of traffic, and Intelligent Transportation Systems (ITS). Benefits for ITS are mainly expected in the field of Advanced Driver Assistance Systems (ADAS), where some assistance/control logics interact with drivers (and their behaviour) and where both drivers' expectations, and impacts of the innovations on drivers' behaviour have to be considered in order to improve: a) the effectiveness of the solutions; b) driving (and traffic) safety and c) acceptance of technological solutions.Modelling of driving behaviour is based on two fundamental requirements. On the one hand, theoretical frameworks and paradigms are needed. On the other, observation tools and data are required in order both to develop/validate theories and to identify modelling parameters for practical applications. If the research focus is on disaggregate driving behaviour rather than aggregate traffic behaviour, the best source of information is based on individual vehicle data (IVD), as typically obtained by instrumented vehicles (IVs). An IV can be described as a standard vehicle where the kinematics, the interaction with surrounding vehicles and the vehicle-driver interaction are recorded for subsequent analysis. The possibility of observing only the kinematics of IVs, as allowed by some camera-based microscopic roadside observation systems like in the NGSIM project (Alexiadis et al., 2004), can lead to a reduced understanding of driving behaviour. Indeed, the possibility of observing the kinematics of an IV is just a prerequisite and IVs are usually equipped with a large number of sensors. Multisensing approaches not only enhance the estimation of the ego-kinematics of the controlled vehicle (Bifulco et al., 2011), but also allow detection of the surrounding traffic conditions and direct monitoring of on-board interaction between the driver and the vehicle, generally via the controlled area network (CAN). The overall result is a more comprehensive observation and enhanced understand...

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“…Most of the experiments on these issues are performed by simulation in the laboratory, because of the difficulty and danger of performing such studies on the road (Hoffmann and Mortimer [3]). The difficulty arises from setting up relative speeds accurately; the danger from the short periods of time-to-collision in some of the experimental conditions (Cavallo and Laurent [4]).…”

Section: Introductionmentioning

confidence: 99%

“…The use of such a simulation may not be a problem, despite limitations with regard to the role of peripheral vision since it is largely the visual information related to the changes in subtended angle that is used for such estimations. When both vehicles are in motion, like in car-following and overtaking, the useful visual information is largely gained from the central visual field of the following driver [3].…”

Section: Introductionmentioning

confidence: 99%

“…Important inter-individual differences have been found in many investigations bearing on drivers' performance on visual perception and ability to estimate the relative position of objects in the visual field [3,[5][6][7][8]. Driving experience may be suspected to be one of the most significant factors explaining these intraindividual differences.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A driving simulator study on the perception of distances in situations of car-following and overtaking

Bergeron¹,

Baumberger²,

Paquette³

et al. 2006

WIT Transactions on the Built Environment, Vol 89

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The principal objective of this experiment was to test how a subject can control his approach towards several simulated car-targets in different contexts of carfollowing and overtaking. We assume that these complex situations may influence driving performance according to the difficulty in perceiving distances properly. A second objective was to evaluate the influence of driving experience in the perception of distances in these situations. Fourteen drivers (7 men and 7 women) between 20 and 56 years old participated to this study. Their driving experience ranged between 2.5 and 32 years and they drove between 1 000 and 25 000 km per year The subjects' first task consisted in stopping their car at equal distance between two static cars separated either by 34.5 or 54.5 m. The second and third tasks were typical manoeuvres of overtaking, subjects having to place their car at equal distance between two preceding cars running at 40 or 60 km/h. Results show that the subjects underestimated distances in any conditions. They were better at adjusting their own position at 60 km/h than at 40 km/h, and their performance increased with smaller car distances. The participants' driving experience (number of years) was the most important individual variable in the differentiation of subjects' performances. These results may be interpreted in terms of the importance of driving experience in situations of car-following and overtaking, in which drivers must estimate the position of other vehicles in reference to their own vehicle.

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Scaling of relative velocity between vehicles

Cited by 89 publications

References 8 publications

Collision Warning Timing, Driver Distraction, and Driver Response to Imminent Rear-End Collisions in a High-Fidelity Driving Simulator

Collision Warning Timing, Driver Distraction, and Driver Response to Imminent Rear-End Collisions in a High-Fidelity Driving Simulator

Experimental evidence supporting simpler Action Point paradigms for car-following

A driving simulator study on the perception of distances in situations of car-following and overtaking

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