Forward Collision Warning: Preliminary Requirements for Crash Alert Timing

LeBlanc, David J.; Kiefer, Raymond J.; Deering, Richard K.; Shulman, M A; Palmer, Melvin D.; Salinger, Jeremy

doi:10.4271/2001-01-0462

Cited by 14 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies on car following often vary in their definition of the car-following task (e.g., Kiefer et al, 1999;LeBlanc et al, 2001). These differences are often the result of the goals of the study.…”

Section: Introductionmentioning

confidence: 99%

Optical Information for Car Following: The Driving by Visual Angle (DVA) Model

Andersen

Sauer

2007

Hum Factors

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Optical Information for Car Following: The Driving by Visual Angle (DVA) Model

Andersen

Sauer

2007

Hum Factors

View full text Add to dashboard Cite

show abstract

“…Fig. 4 illustrates a collision notification, which is often used as a warning signal for rear-end collision warning systems [4]. It provides sufficient information about the critical situation and is self-explanatory.…”

Section: A) Warning Trianglementioning

confidence: 99%

“…One main reason for rear-end collisions is driver inattentiveness [4]. Robert Bosch GmbH uses driver monitoring for its Predictive Collision Warning System to determine an appropriate warning point in time.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of forward collision warnings for different driver attention states

Ovcharova

Fausten

Gauterin

2012

2012 IEEE Intelligent Vehicles Symposium

View full text Add to dashboard Cite

In recent years various collision warning systems for rear-end crash situations have been introduced into the market. Their goal is to reduce the number of fatalities and to mitigate collision consequences. The challenge in the design for such functions is to achieve a high effectiveness for both attentive and inattentive drivers. A possible approach could be an adaptation of the human-machine interface (HMI) to driver attentiveness levels.This paper presents a preliminary study of different visualacoustical HMI concepts performed in a driving simulator. The participants completed two drives with each warning signal. In the first drive they were distracted by a secondary task and in the second one they were in an attentive state. The statistical analysis of the inattentive drivers' data showed a significant reaction time reduction by employing an acoustical speech warning. A visual alert for inattentive subjects did not result in an additional reduction. Only a simple and intuitive visual display showed a significant reduction of response time for attentive participants. The results of this study show that for varying driver attentiveness levels a warning intensity adaptation is needed in order to increase the benefit and acceptance of the collision warning.

show abstract

“…The algorithms utilize kinematic models to refine motion parameters (i.e. range, speed and acceleration), and then generate warnings by comparing the results with certain thresholds, such as warning distance thresholds [5][6] [7], range vs. range-rate boundaries [8] [9], deceleration vs. braking-application-time boundaries [10]. As being pointed out in [11], the assumptions that the algorithms usually make, e.g.…”

Section: A Review Of Fcws Warning Algorithmsmentioning

confidence: 99%

Qualitative Modeling of Vehicle Behavior for Scenario Parsing

Zheng

Zhang

Meng

et al. 2006

2006 IEEE Intelligent Transportation Systems Conference

View full text Add to dashboard Cite

In most present frontal collision warning systems (FCWS), the warning algorithm mainly depends on simple combination of linearly predicted vehicle-motion parameters. Such systems suffer from high false-alarm-rate due to the incapability of identifying different transportation scenarios. Scenario parsing deals with such problems by analyzing transportation scenarios and applying specific threat assessment algorithm to each scenario. In this paper, approaches of symbolization and pattern/rule data mining are presented. Real-data experiments demonstrate that the approaches are effective.Index terms-scenario parsing, data mining, collision warning systems (CWS), qualitative modeling, vehicle behavior

show abstract

Forward Collision Warning: Preliminary Requirements for Crash Alert Timing

Cited by 14 publications

References 2 publications

Optical Information for Car Following: The Driving by Visual Angle (DVA) Model

Optical Information for Car Following: The Driving by Visual Angle (DVA) Model

Effectiveness of forward collision warnings for different driver attention states

Qualitative Modeling of Vehicle Behavior for Scenario Parsing

Contact Info

Product

Resources

About