Braking from different speeds: Judgments of collision speed if a car does not stop in time

Svenson, Ola; Eriksson, Gabriella; Gonzalez, Nichel

doi:10.1016/j.aap.2011.08.015

Cited by 21 publications

(4 citation statements)

References 14 publications

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“…Finally, on a more general note, further research on real-time coaching programs can investigate reducing misperception of braking capabilities [58,59] and compare the monetary incentives involved in such programs (which include both private and societal benefits, see Section 1.2) with the potential willingness-to-pay to avoid the so-called "time saving bias", as shown by Tscharaktschiew [60].…”

Section: Discussionmentioning

confidence: 99%

Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior

Orsini

Tagliabue

et al. 2021

Sustainability

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Real-time coaching programs are designed to give feedback on driving behavior to usage-based motor insurance users; they are often general purpose programs that aim to promote smooth driving. Here, we investigated the effect of different on-board real-time coaching programs on the driving behavior on highway deceleration lanes with a driving simulator experiment. The experiment was organized into two trials. The first was a baseline trial, in which participants drove without receiving any feedback; a cluster analysis was then performed to divide participants into two groups, based on their observed driving style. One month later, a second trial was carried out, with participants driving on the same path as the first trial, this time receiving contingent feedback related to their braking/acceleration behavior. Four feedback systems were tested; overall, there were eight experimental groups, depending on the clustered driving style (aggressive and defensive), feedback modality (visual and auditory), and feedback valence (positive and negative). Speed, deceleration, trajectory, and lateral control variables, collected before and onto the deceleration lane, were investigated with mixed ANOVAs, which showed that the real-time coaching programs significantly reduced speeds and maximum deceleration values, while improving lateral control. A change toward a safer exit strategy (i.e., entering the lane before starting to decelerate) was also observed in defensive drivers.

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Section: Discussionmentioning

confidence: 99%

Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior

Orsini

Tagliabue

et al. 2021

Sustainability

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“…Thus, a surrogate safety measure, time-to-collision (TTC), was adopted in this study. It represents the time required for two successive vehicles, occupying the same lane, to collide if they continue at their present speed when vehicle i moves faster than leading vehicle i −l ( 27 , 28 ):…”

Section: Methodsmentioning

confidence: 99%

Deep Reinforcement Learning-Based Vehicle Driving Strategy to Reduce Crash Risks in Traffic Oscillations

et al. 2020

Transportation Research Record: Journal of the Transportation R

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The primary objective of this study is to propose a deep reinforcement learning-based driving strategy for individual vehicles to mitigate oscillations and optimize traffic safety in stop-and-go waves. A deep deterministic policy gradient (DDPG)-based driving strategy, which requires information that is directly obtained by in-vehicle sensors, is proposed for system performance optimization. Two typical scenarios were simulated based on simulation software (SUMO): (i) the leading vehicle slowed down according to real trajectory data to produce one oscillation; (ii) the leading vehicle conducted several abrupt decelerations with various degrees of disturbance to produce multiple oscillations. The DDPG agents interacted with the SUMO platform to determine the optimal acceleration of vehicles that can reduce crash risks in various stop-and-go waves. The results showed that the proposed DDPG-based driving strategy successfully reduced the crash risk by 68.9%–78.4%. Scenarios with different penetration rates of DDPG agents and in various flow rates were compared to test the effect of the proposed strategy. The DDPG-based driving strategy reduced crash risk more with the increase of penetration rate and this strategy performed better when applied in the scenario with a high traffic flow rate. The proposed strategy is compared with the adaptive cruise control and jam-absorbing driving strategies. Results showed the proposed strategy outperformed other oscillation mitigating strategies in reducing crash risks.

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“…In addition to an alert through an auditory interface, the vehicle is also equipped with a self‐braking function [3]. Despite velocity information, the time‐to‐collision (TTC) is another commonly used approach for perception‐based collision warning [4]. TTC represents the time to collide if two vehicles keep their current speed

T T C false(k false) = ({, \begin{matrix} 1em4pt \\ \frac{x_{2} false(k false) - x_{1} false(k false)}{v_{2} false(k false) - v_{1} false(k false)}, & i f v_{2} > v_{1} \\ normal∞, & i f v_{2} \leq v_{1} \end{matrix})

A smaller TTC indicates a more dangerous traffic condition and vice versa.…”

Section: Introductionmentioning

confidence: 99%

Investigation of a DSRC‐based end of queue collision warning system by considering real freeway data

Liu

Wang

Cai

2018

IET Intelligent Transport Systems

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Braking from different speeds: Judgments of collision speed if a car does not stop in time

Cited by 21 publications

References 14 publications

Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior

Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior

Deep Reinforcement Learning-Based Vehicle Driving Strategy to Reduce Crash Risks in Traffic Oscillations

Investigation of a DSRC‐based end of queue collision warning system by considering real freeway data

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