Automated Vehicle Disengagement Reaction Time Compared to Human Brake Reaction Time in Both Automobile and Motorcycle Operation

Dinges, Jeffrey; Durisek, Nicholas J.

doi:10.4271/2019-01-1010

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Disengagement reaction time was available in the 2015-2017 disengagement reports, and was another important information studied in past literature (Banerjee et al, 2018;Dinges and Durisek, 2019;Dixit et al, 2016). The California DMV defined disengagement reaction time as "the period of time elapsed from when the autonomous vehicle test driver was alerted of the technology failure, and the driver assumed manual control of the vehicle" (Banerjee et al, 2018).…”

Section: Patterns In Av Disengagementsmentioning

confidence: 99%

“…The California DMV defined disengagement reaction time as "the period of time elapsed from when the autonomous vehicle test driver was alerted of the technology failure, and the driver assumed manual control of the vehicle" (Banerjee et al, 2018). The average disengagement reaction time was estimated to be 0.83 s -0.87 s (Banerjee et al, 2018;Dinges and Durisek, 2019;Dixit et al, 2016). The average disengagement reaction time is between the average automobile brake reaction time of 1.13 s and the average motorcycle brake reaction time of 0.60 s (Dinges and Durisek, 2019).…”

Section: Patterns In Av Disengagementsmentioning

confidence: 99%

“…The average disengagement reaction time was estimated to be 0.83 s -0.87 s (Banerjee et al, 2018;Dinges and Durisek, 2019;Dixit et al, 2016). The average disengagement reaction time is between the average automobile brake reaction time of 1.13 s and the average motorcycle brake reaction time of 0.60 s (Dinges and Durisek, 2019). Disengagement reaction time is expected to increase with test operators becoming more comfortable and gaining trust in the AV's handling of risky situations on roads (Banerjee et al, 2018;Dinges and Durisek, 2019).…”

Section: Patterns In Av Disengagementsmentioning

confidence: 99%

See 2 more Smart Citations

Automated vehicle crash sequences: Patterns and potential uses in safety testing

Song

Chitturi

Noyce

2021

Accident Analysis & Prevention

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With safety being one of the primary motivations for developing automated vehicles (AVs), extensive field and simulation tests are being carried out to ensure AVs can operate safely on roadways. Since 2014, the California Department of Motor Vehicles (DMV) has been collecting AV collision and disengagement reports, which are valuable data sources for studying AV crash patterns. A crash sequence of events describes the AV's interactions with other road users before a collision in a temporal manner. In this study, sequence of events data extracted from California AV collision reports were used to investigate patterns and how they may be used to develop AV test scenarios. Employing sequence analysis methods and clustering, this study evaluated 168 AV crashes (with AV in automatic driving mode before disengagement or collision) reported to the California DMV from 2015 to 2019. Analysis of subsequences showed that the most representative pattern in AV crashes was "collision following AV stop". Analysis of event transition showed that disengagement, as an event in 24% of all studied AV crash sequences, had a transition probability of 68% to an immediate collision. Cluster analysis characterized AV crash sequences into seven groups with distinctive crash dynamic features. Cross-tabulation analysis showed that sequence groups were significantly associated with variables measuring crash outcomes and describing environmental conditions. Crash sequences are useful for developing AV test scenarios. Based on the findings, a scenario-based AV safety testing framework was proposed with sequence of events embedded as a core component.

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Section: Patterns In Av Disengagementsmentioning

confidence: 99%

Section: Patterns In Av Disengagementsmentioning

confidence: 99%

Section: Patterns In Av Disengagementsmentioning

confidence: 99%

See 1 more Smart Citation

Automated vehicle crash sequences: Patterns and potential uses in safety testing

Song

Chitturi

Noyce

2021

Accident Analysis & Prevention

View full text Add to dashboard Cite

show abstract

“…The reaction time of AVs is considerably shorter when compared to humans. Studies on existing Autonomy Level 2 and 3 vehicles [9], showed that AVs can react on average in ~0.66 s while humans require over 0.9 s or even 1.5 s, in impaired scenarios. Moving towards full autonomy and CAVs, these numbers are expected to decrease even further.…”

Section: System Descriptionmentioning

confidence: 99%

On Urban Traffic Flow Benefits of Connected and Automated Vehicles

Mavromatis,

Tassi,

Piechocki

et al. 2020

Preprint

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Automated Vehicles are an integral part of Intelligent Transportation Systems (ITSs) and are expected to play a crucial role in the future mobility services. This paper investigates two classes of self-driving vehicles: (i) Level 4&5 Automated Vehicles (AVs) that rely solely on their on-board sensors for environmental perception tasks, and (ii) Connected and Automated Vehicles (CAVs), leveraging connectivity to further enhance perception via driving intention and sensor information sharing. Our investigation considers and quantifies the impact of each vehicle group in large urban road networks in Europe and in the USA. The key performance metrics are the traffic congestion, average speed and average trip time. Specifically, the numerical studies show that the traffic congestion can be reduced by up to a factor of four, while the average flow speeds of CAV group remains closer to the speed limits and can be up to 300% greater than the human-driven vehicles. Finally, traffic situations are also studied, indicating that even a small market penetration of CAVs will have a substantial net positive effect on the traffic flows.

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A comparative simulator study of reaction times to yellow traffic light under manual and automated driving

Rossi

Gastaldi

Orsini

et al. 2021

Transportation Research Procedia

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Automated Vehicle Disengagement Reaction Time Compared to Human Brake Reaction Time in Both Automobile and Motorcycle Operation

Cited by 4 publications

References 11 publications

Automated vehicle crash sequences: Patterns and potential uses in safety testing

Automated vehicle crash sequences: Patterns and potential uses in safety testing

On Urban Traffic Flow Benefits of Connected and Automated Vehicles

A comparative simulator study of reaction times to yellow traffic light under manual and automated driving

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