Ready for Take-Over? A New Driver Assistance System for an Automated Classification of Driver Take-Over Readiness

Braunagel, Christian; Rosenstiel, Wolfgang; Kasneci, Enkelejda

doi:10.1109/mits.2017.2743165

Cited by 116 publications

(58 citation statements)

References 14 publications

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“…Considering the TOR caused by loss of road marking, the stability of the trajectory was evaluated. Performing lane change in situation in which is not required is usually 720 considered a low-quality take over [44]. In the driving scenario, given that the width of the lane was lane w = 3m and the width of the car was car w = 2m, the maximum possible distance from the lane center beyond which the car does not cross the separation line is d = 0.75m.…”

Section: Deviation From Lane Centermentioning

confidence: 99%

Get ready for automated driving using Virtual Reality

Sportillo

Paljic

Ojeda

2018

Accident Analysis & Prevention

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In conditionally automated vehicles, drivers can engage in secondary activities while traveling to their destination. However, drivers are required to appropriately respond, in a limited amount of time, to a take-over request when the system reaches its functional boundaries. Interacting with the car in the proper way from the first ride is crucial for car and road safety in general. For this reason, it is necessary to train drivers in a risk-free environment by providing them the best practice to use these complex systems. In this context, Virtual Reality (VR) systems represent a promising training and learning tool to properly familiarize drivers with the automated vehicle and allow them to interact with the novel equipment involved. In addition, Head-Mounted Display (HMD)-based VR (light VR) would allow for the easy deployment of such training systems in driving schools or car dealerships. In this study, the effectiveness of a light Virtual Reality training program for acquiring interaction skills in automated cars was investigated. The effectiveness of this training was compared to a user manual and a fixed-base simulator with respect to both objective and self-reported measures. Sixty subjects were randomly assigned to one of the systems in which they went through a training phase followed by a test drive in a high-end driving simulator. Results show that the training system affects the take-over performances. Moreover, self-reported measures indicate that the light VR training is preferred with respect to the other systems. Finally, another important outcome of this research is the evidence that VR plays a strategic role in the definition of the set of metrics for profiling proper driver interaction with the automated vehicle.

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Section: Deviation From Lane Centermentioning

confidence: 99%

Get ready for automated driving using Virtual Reality

Sportillo

Paljic

Ojeda

2018

Accident Analysis & Prevention

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“…While electroencephalogram (EEG) sensors allow for the most faithful representation of the driver's brain activity [34]- [36], they are too intrusive to be viable in commercial vehicles. Another approach used in recent studies [32] is to define take-over readiness based on take-over time and takeover quality in experimental trials with take-over requests issued to drivers performing secondary activities. However, the nature of the task restricts such experiments to simulator settings.…”

Section: Introductionmentioning

confidence: 99%

Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness

Deo¹,

Trivedi²

2020

IEEE Trans. Intell. Veh.

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Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles. In this paper, we propose a data-driven approach for estimating the driver's take-over readiness based purely on observable cues from invehicle vision sensors. We present an extensive naturalistic drive dataset of drivers in a conditionally autonomous vehicle running on Californian freeways. We collect subjective ratings for the driver's take-over readiness from multiple human observers viewing the sensor feed. Analysis of the ratings in terms of intra-class correlation coefficients (ICCs) shows a high degree of consistency in the ratings across raters. We define a metric for the driver's take-over readiness termed the 'Observable Readiness Index (ORI)' based on the ratings. Finally, we propose an LSTM model for continuous estimation of the driver's ORI based on a holistic representation of the driver's state, capturing gaze, hand, pose and foot activity. Our model estimates the ORI with a mean absolute error of 0.449 on a 5 point scale.

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“…The rich sensing information of the environment provided by the cameras can also be used to extend the functions of driver assistance systems. Some applications under investigation include traffic light detection [ 8 ], traffic sign recognition [ 9 ], vehicle identification [ 10 ], vehicle speed detection [ 11 ], pedestrian recognition [ 12 ], overtaking detection [ 13 ], and parking assistance [ 14 ], etc. Thus, despite the depth measurement precision under varying illumination conditions, it still has great potential to use image-based approaches for the vehicular system development.…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Lin

Dai

et al. 2020

Sensors

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One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.

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Ready for Take-Over? A New Driver Assistance System for an Automated Classification of Driver Take-Over Readiness

Cited by 116 publications

References 14 publications

Get ready for automated driving using Virtual Reality

Get ready for automated driving using Virtual Reality

Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

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