Driver perception and reaction in collision avoidance: Implications for ADAS development and testing

Sieber, Markus; Färber, Berthold

doi:10.1109/ivs.2016.7535392

Cited by 16 publications

(10 citation statements)

References 7 publications

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“…They also conclude that a large-scale deployment of collision warnings system will not occur until the nuisance and false warnings generated by the application are minimized, thereby increasing driver acceptance. A 1.1 s TTC at an average speed of 50 km/h was used in [ 72 ]. In studies that do not monitor the driver’s attention, a higher TTC is generally used to compensate for the potential inattention of the driver.…”

Section: Methodsmentioning

confidence: 99%

Driving Performance and Technology Acceptance Evaluation in Real Traffic of a Smartphone-Based Driver Assistance System

Voinea

Postelnicu

Duguleană

et al. 2020

IJERPH

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Technological advances are changing every aspect of our lives, from the way we work, to how we learn and communicate. Advanced driver assistance systems (ADAS) have seen an increased interest due to the potential of ensuring a safer environment for all road users. This study investigates the use of a smartphone-based ADAS in terms of driving performance and driver acceptance, with the aim of improving road safety. The mobile application uses both cameras of a smartphone to monitor the traffic scene and the driver’s head orientation, and offers an intuitive user interface that can display information in a standard mode or in augmented reality (AR). A real traffic experiment consisting of two driving conditions (a baseline scenario and an ADAS scenario), was conducted in Brasov, Romania. Objective and subjective data were recorded from twenty-four participants with a valid driver’s license. Results showed that the use of the ADAS influences the driving performance, as most of them adopted an increased time headway and lower mean speeds. The technology acceptance model (TAM) questionnaire was used to assess the users’ acceptance of the proposed driver assistance system. The results showed significant interrelations between acceptance factors, while the hierarchical regression analysis indicates that the variance of behavioral intention (BI) can be predicted by attitude toward behavior.

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Section: Methodsmentioning

confidence: 99%

Driving Performance and Technology Acceptance Evaluation in Real Traffic of a Smartphone-Based Driver Assistance System

Voinea

Postelnicu

Duguleană

et al. 2020

IJERPH

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“…A set of minimum performance criteria (MPC) was established for the primary forward-facing sensor, as shown in Table 1. The criteria for minimum range requirements (and corresponding sensor update rates) were determined based on direct feedback and published articles from mine-site personnel involved with developing a response to the Moura No 2 mine disaster [2], complemented by published research into human reaction times for collision avoidance [7]. A minimum effective range of at least 20 m was specified by the ACARP industry project monitors as sufficient for vehicle navigation in an emergency due to the low speeds expected to be employed by operators under these circumstances.…”

Section: Sensor Selectionmentioning

confidence: 99%

Mine Machine Radar Sensor for Emergency Escape

Hargrave

Munday

Kennedy³

et al. 2020

Resources

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This paper presents the results of recent work to develop and trial a mine machine radar sensor for underground coal mine vehicles. There is an urgent industry need for an integrated solution to the problem of operating an underground vehicle in conditions of dense ambient dust and/or smoke, such as may occur in underground coal mines after a fire or explosion. Under these conditions, sensors such as cameras and lidar offer limited assistance due to their inability to penetrate thick dust. Thermal infrared can penetrate dust but still results in poor vision, as there is insufficient temperature contrast between the tunnel walls and the ambient air. Microwave radar sensors are able to penetrate the dust, and suitable radar sensors have been developed for use in the automation industry. Adapting such sensors for use in an underground coal mining environment was the focus of this research effort, and involved trialing a suitable sensor in dust and smoke chambers as well as trials in an underground coal mine with introduced dust. Data processing and the development of a suitable user interface were key aspects of the research. Since any sensor would have to operate in an explosive atmosphere, a related research work developed a flameproof dielectric enclosure to allow the use of the radar in the mine environment.

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“…The test experiment of 101 volunteers confirm that nondriving tasks will increase the response time of the corresponding take-over request, and the initial response time of tactile and auditory take-over requests is lower than that of visual. In a traffic accident scenario, the drivers' reaction is related to the speed of obstacles before the accident [29], and the drivers' reaction time is linearly related to the collision time [30]. Xue et al [31] analyze the simulation data of 47 volunteers in simulated carfollowing scenarios and found that in the case of high traffic flow density, the response time of drivers is usually shorter than that of low to medium traffic density [32,33], while the response time of male and nonprofessional drivers tends to be slightly longer [34].…”

Section: Drivers' Reaction Timementioning

confidence: 99%

DeepCF: A Deep Feature Learning-Based Car-Following Model Using Online Ride-Hailing Trajectory Data

Xie

Alfarraj

et al. 2020

Wireless Communications and Mobile Computing

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The car-following model describes the microscopic behavior of the vehicle. However, the existing car-following models set the drivers’ reaction time to a fixed value without considering its dynamics. In order to improve the accuracy of car-following model, this paper proposes Deep Feature Learning-based Car-Following Model (DeepCF), a car-following model based on fatigue driving and Generative Adversarial Networks (GAN). The model is composed of the drivers’ reaction time model and the car-following decision algorithm. First, we regard driving fatigue as the starting point to study the influence of driving time and the acceleration of the preceding vehicle on the drivers’ reaction time, and develop a coarse-grained drivers’ reaction time model. Secondly, considering the impact of fatigue driving on car-following decisions, we utilize GAN to generate a driving decision database based on reaction time and use Euclidean distance as a decision search indicator. Finally, we conduct experiments on a real data set, and the results indicate that our DeepCF model is superior to baseline models.

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Driver perception and reaction in collision avoidance: Implications for ADAS development and testing

Cited by 16 publications

References 7 publications

Driving Performance and Technology Acceptance Evaluation in Real Traffic of a Smartphone-Based Driver Assistance System

Driving Performance and Technology Acceptance Evaluation in Real Traffic of a Smartphone-Based Driver Assistance System

Mine Machine Radar Sensor for Emergency Escape

DeepCF: A Deep Feature Learning-Based Car-Following Model Using Online Ride-Hailing Trajectory Data

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