A Human-Vehicle Collaborative Driving Framework for Driver Assistance

Tran, Duy; Du, Jianhao; Sheng, Weihua; Osipychev, Denis; Sun, Yaqi; Bai, He

doi:10.1109/tits.2018.2878027

Cited by 51 publications

(16 citation statements)

References 23 publications

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“…Although these products can be viewed as active safety systems, they still interact with the human drivers passively since most of these systems are unable to monitor and understand driver behaviors in real-time. The dynamic interaction and mutual understanding between the driver and the intelligent units are expected as one of the primary objectives for the development of the next-generation ADAS [11] [12].…”

Section: A Motivationmentioning

confidence: 99%

An ensemble deep learning approach for driver lane change intention inference

Xing

Wang

Cao

et al. 2020

Transportation Research Part C: Emerging Technologies

156

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With the rapid development of intelligent vehicles, drivers are increasingly likely to share their control authorities with the intelligent control unit. For building an efficient Advanced Driver Assistance Systems (ADAS) and shared-control systems, the vehicle needs to understand the drivers' intent and their activities to generate assistant and collaborative control strategies. In this study, a driver intention inference system that focuses on the highway lane change maneuvers is proposed. First, a high-level driver intention mechanism and framework are introduced. Then, a vision-based intention inference system is proposed, which captures the multi-modal signals based on multiple low-cost cameras and the VBOX vehicle data acquisition system. A novel ensemble bi-directional recurrent neural network (RNN) model with Long Short-Term Memory (LSTM) units is proposed to deal with the time-series driving sequence and the temporal behavioral patterns. Naturalistic highway driving data that consists of lane-keeping, left and right lane change maneuvers are collected and used for model construction and evaluation. Furthermore, the driver's pre-maneuver activities are statistically analyzed. It is found that for situation-aware, drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver, and the time interval between steering the handwheel and crossing the lane is about 2 s on average. Finally, hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones. With five-fold cross-validation, the EBiLSTM model achieves an average accuracy of 96.1% for the intention that is inferred 0.5 s before the maneuver starts.

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Section: A Motivationmentioning

confidence: 99%

An ensemble deep learning approach for driver lane change intention inference

Xing

Wang

Cao

et al. 2020

Transportation Research Part C: Emerging Technologies

156

View full text Add to dashboard Cite

show abstract

“…Additionally, the characterization of different driver behaviors and the adjustment of the controller gains based on these factors will be key to the acceptance of these systems among drivers with different driving styles. Finally, the integration of shared controllers with other modules, such as driver monitoring systems, visual interfaces, and collision detection systems, will be necessary for the establishment of a complete collaborative driver-automation framework [162], [163].…”

Section: Discussion and Perspectivementioning

confidence: 99%

A Review of Shared Control for Automated Vehicles: Theory and Applications

Marcano

Díaz

Pérez

et al. 2020

IEEE Trans. Human-Mach. Syst.

156

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The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory-and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driverin-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.

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“…The more fatigued the system is (i.e., it is deprived of sleep), the greater the need for sleep, indicating that sleepiness will vary. Scientific bodies such as the Sleep Laboratory [27], the Sleep Disorders Division [28], and the Sleep Specialist Association [29], among others, have developed different descriptive standards of sleepiness. Present subjective methods are based on questionnaires and sleep electrophysiological tests used to test sleepiness.…”

Section: ) Subjective Methodsmentioning

confidence: 99%