Effectiveness of Flashing Brake and Hazard Systems in Avoiding Rear-End Crashes

Li, Guofa; Wang, Wenjun; Li, Shengbo Eben; Cheng, Bo; Green, Paul

doi:10.1155/2014/792670

Cited by 30 publications

(29 citation statements)

References 23 publications

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“…4, the time needed for detection were all about 0.8 s for either pedestrians, cyclists, or vehicles. As indicated in previous studies, drivers' brake response time to imminent dangers was about 1.0 s [31]. The detection time of the proposed method in this study is shorter than drivers' brake response time, which may help a lot in the design of ADASs.…”

Section: Selected Features and Detection Resultssupporting

confidence: 50%

Detection of road traffic participants using cost-effective arrayed ultrasonic sensors in low-speed traffic situations

Zou

et al. 2019

Mechanical Systems and Signal Processing

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Effective detection of traffic participants is crucial for driver assistance systems. Traffic safety data reveal that the majority of preventable pedestrian fatalities occurred at night. The lack of light at night may cause dysfunction of sensors like cameras. This paper proposes an alternative approach to detect traffic participants using cost-effective arrayed ultrasonic sensors. Candidate features were extracted from the collected episodes of pedestrians, cyclists, and vehicles. A conditional likelihood maximization method based on mutual information was employed to select an optimized subset of features from the candidates. The belonging probability to each group along with time was determined based on the accumulated object type attributes outputted from a support vector machine classifier at each time step. Results showed an overall detection accuracy of 86%, with correct detection rate of pedestrians, cyclists and vehicles around 85.7%, 76.7% and 93.1%, respectively. The time needed for detection was about 0.8 s which could be further shortened when the distance between objects and sensors was shorter. The effectiveness of arrayed ultrasonic sensors on objects detection would provide all-around-the-clock assistance in low-speed situations for driving safety.

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Section: Selected Features and Detection Resultssupporting

confidence: 50%

Detection of road traffic participants using cost-effective arrayed ultrasonic sensors in low-speed traffic situations

Zou

et al. 2019

Mechanical Systems and Signal Processing

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“…Apparatus. The experiment was conducted in the driving simulator of Tsinghua University as Figure 1 shows [21]. The driving simulator consists of a visual simulation unit, an audio simulation unit, and a motion simulation unit.…”

Section: Main Experiment: Braking Behaviors In Different Motion Patternsmentioning

confidence: 99%

Drivers’ Braking Behaviors in Different Motion Patterns of Vehicle-Bicycle Conflicts

Hou

Duan

Wang

et al. 2019

Journal of Advanced Transportation

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Bicycling is one of the popular modes of transportation, but bicyclists are easily involved in injuries or fatalities in vehicle-bicycle (V-B) accidents. The AEB (Autonomous Emergency Braking) systems have been developed to avoid collisions, but their adaptiveness needs to be further improved under different motion patterns of V-B conflicts. This paper analyzes drivers’ braking behaviors in different motion patterns of V-B conflicts to improve the performance of Bicyclist-AEB systems. For safety and data reliability, a driving simulator was used to reconstruct two typical conflict types, i.e., SCR (a bicycle crossing the road from right in front of a straight going car) and SSR (a bicycle cut-in from right in front of a straight going car). Either conflict contained various parameterized motion patterns, which were characterized by a combination of parameters: Vc (car velocity), TTC (time-to-collision), Vb (bicycle velocity), and Dlat (lateral distance between the car and the bicycle) or Vlat (maximum lateral velocity of the bicycle). Some 26 licensed drivers participated in an orthogonal experiment for braking behavior analysis. Results revealed that drivers brake immediately when V-B conflicts occur; hence the BRT (brake reaction time) is independent of any motion pattern parameters. This was further verified by another orthogonal experiment with 10 participants using the eye tracking device. BRT in SSR is longer than that in SCR due to the less perceptible risk and drivers’ lower expectation of a collision. The braking intensity and brake Pedal Speed are higher in short-TTC patterns in both conflict types. Therefore, TTC is not a proper activation threshold but a reasonable indicator of braking intensity and Pedal Speed for driver-adaptive AEB systems. By applying the findings in the Bicyclist-AEB, the adaptiveness and acceptability of Bicyclist-AEB systems can be improved.

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“…A vehicle accelerates (r a ) or decelerates (r d ) according to Equation 4: behaviour with a model (under certain conditions, e.g. with specific values of the variables considered in [6]), it must support (at least) the maximum response time required (by humans) to initiate the vehicle deceleration. In [7], a headway threshold was considered to choose between free flow and a car-following regime; the former was modelled considering a random term (associated with the follower acceleration at time t), the stimulus (relative speed between leader and follower at time t-x), and the sensitivity (a function of the follower speed, space headway, and traffic density at t-px), where t is the current time, x is the driver reaction time, and p is used to regulate the delay.…”

Section: Acceleration (Deceleration) Modelmentioning

confidence: 99%

“…The model is implemented according to the relation between the vehicle deceleration and the passenger comfort levels. In [6], the velocity, time gap, deceleration, and braking system response were the variables selected that influence the follower's braking response time, and it was found that the time gap has the greatest influence, with response times of 1.34, 1.67, and 2.23 s for time gaps of 1, 2, and 3 s, respectively. Then, if the objective is to imitate human should pass the intersection, the vehicles approaching from each direction have been compared (the variables involved are the distance to the intersection, braking distance, current speed, and deceleration capacity).…”

Section: Introductionmentioning

confidence: 99%

Acceleration (Deceleration) Model Supporting Time Delays to Refresh Data

Carrillo-González

Lizárraga²,

Barbosa-Santillán³

2018

PROMET

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This paper proposes a mathematical model to regulate the acceleration (deceleration) applied by self-driving vehicles in car-following situations. A virtual environment is designed to test the model in different circumstances: (1) the followers decelerate in time if the leader decelerates, considering a time delay of up to 5 s to refresh data (vehicles position coordinates) required by the model, (2) with the intention of optimizing space, the vehicles are grouped in platoons, where 3 s of time delay (to update data) is supported if the vehicles have a centre-to-centre spacing of 20 m and a time delay of 1 s is supported at a spacing of 6 m (considering a maximum speed of 20 m/s in both cases), and (3) an algorithm is presented to manage the vehicles’ priority at a traffic intersection, where the model regulates the vehicles’ acceleration (deceleration) and a balance in the number of vehicles passing from each side is achieved.

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Effectiveness of Flashing Brake and Hazard Systems in Avoiding Rear-End Crashes

Cited by 30 publications

References 23 publications

Detection of road traffic participants using cost-effective arrayed ultrasonic sensors in low-speed traffic situations

Detection of road traffic participants using cost-effective arrayed ultrasonic sensors in low-speed traffic situations

Drivers’ Braking Behaviors in Different Motion Patterns of Vehicle-Bicycle Conflicts

Acceleration (Deceleration) Model Supporting Time Delays to Refresh Data

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