Driver assistance system for lane departure avoidance by steering and differential braking

Enache, Nicoleta Minoiu; Mammar, Saïd; Glaser, Sébastien; Lusetti, Benoît

doi:10.3182/20100712-3-de-2013.00011

Cited by 16 publications

(5 citation statements)

References 4 publications

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“…where t is current time, t is the time step, and α is a weighting coefficient that adjusts the weighting of the calculation of (5) and (9).…”

Section: Potential Field Modelmentioning

confidence: 99%

“…In some driver assistance systems for lane departure avoidance, the system will take over if driver inattentiveness as well as the risk of lane departure are found. For such systems, methods to switch between the driver and the system have been studied [9]- [11]. Different collision avoidance methods have been proposed for unmanned aerial vehicles (UAV) or mobile robots.…”

Section: Introductionmentioning

confidence: 99%

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Realization and Evaluation of an Instructor-Like Assistance System for Collision Avoidance

Chen

Yamaguchi

Okuda

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Advanced driver assistance systems should not only make the driving experience safer and more comfortable, it should also have a positive effect on driving behaviors. In this paper, an instructor-like assistance system for collision avoidance is developed and realized on an actual vehicle. The proposed system is activated only if the driver is not operating the vehicle properly when facing a collision risk. The vehicle control is shared by the driver and the assistance system. It is controlled by servomotors. In order to fulfill this requirement, a constraint satisfaction problem (CSP) is proposed and solved based on safe driving constraints and predictive vehicle states. Vehicle motion is predicted by a combination of a dynamics model and a potential field model that reflects the driver's risk feeling to an obstacle. Improved driving behavior is verified and evaluated quantitatively based on driving simulator data. By comparing the driving data before and after using the assistance system, it is found that distance is increased and speed is reduced when passing an obstacle. As a result, driving behavior becomes safer for collision avoidance due to the system's instruction. Furthermore, an experiment with an actual vehicle also demonstrates the practicability of the control system and shows the influence of different safe driving constraints.

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“…where t is current time, t is the time step, and α is a weighting coefficient that adjusts the weighting of the calculation of (5) and (9).…”

Section: Potential Field Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Realization and Evaluation of an Instructor-Like Assistance System for Collision Avoidance

Chen

Yamaguchi

Okuda

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…In [11], an adaptive longitudinal driving assistance system include adaptive cruise control and forward collision avoidance system considering driver behavior and characteristic is proposed and a recursive least square self-learning algorithm for driver characteristic modeling is introduced. In [12], a lane departure avoidance system is applied to prevent the driver from inattention lane change based on the combination of lateral active steering controller and longitudinal differential braking controller. In [13], this paper studies driver braking intention on the testbed of commercial vehicles with multiple drivers.…”

Section: Introductionmentioning

confidence: 99%

Recognizing Driver Braking Intention with Vehicle Data Using Unsupervised Learning Methods

Xing

Wang

et al. 2017

SAE Technical Paper Series

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Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared. Additionally, the importance evaluation of features from raw dataset respect to driving maneuvers clustering will be proposed. The experiment data are collected from a pure electric vehicle in real world. Final results show that the proposed method can detect driver's braking intention in a very beginning moment with a high accuracy and the most important features for driving maneuver clustering are selected.

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“…Each of these systems has been proven to help to reduce the occurrence of ROR crashes within particular scenarios. [10][11][12][13] While these safety systems can offer better reductions in single-vehicle ROR crashes than roadway modifications can, they suffer from similar limitations. Each of these systems performs well when the driver behaves within nominal limits but, for more extreme departure cases and poor performance by the driver, the effect of these systems is reduced.…”

Section: Introductionmentioning

confidence: 99%

Validation of a fixed-base automotive simulator for run-off-road safety and recovery training

Freeman

Rodriguez

Wagner

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part D:

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Traffic fatalities and injuries continue to demand the attention of researchers and governments across the world as they remain significant factors in public health and safety. Enhanced legislature, together with vehicle and roadway technology, has helped to reduce the impact of traffic crashes in many scenarios. However, one specifically troublesome area of traffic safety which persists is the run-off-road crash where a vehicle's wheels leave the paved portion of the roadway and begin to travel on the shoulder or side of the road. Large percentages of fatal and injury traffic crashes are attributed to run-off-road events. One of the most critical reasons why run-off-road scenarios quickly evolve into serious crashes is poor driver performance. Drivers are unprepared to handle the situation safely and often execute dangerous maneuvers, such as overcorrection, which can lead to devastating results. One countermeasure which directly addresses the driver performance is driver education and training. In this article, a simulator-based driving environment is proposed specifically for run-off-road recovery training. A human subject study is used to validate the simulator as an effective tool for replicating the run-off-road experience with the additional benefit of receiving insight into driver reactions to run-offroad events. Analysis of variance (ANOVA) results of subjective questionnaire data and objective performance evaluation parameters show strong correlations to run-off-road crash data and previous run-off-road study conclusions. In particular, higher vehicle velocities, curved roads, and larger differences between the friction coefficient of the road and the friction coefficient of the shoulder all negatively impacted drivers' recoveries from run-off-road scenarios. The only nonsignificant impact found was that of the roadway edge, indicating a possible limitation of the simulator system with respect to that particular environment variable. The validation study provides a foundation for further evaluation and development of a simulator-based run-off-road recovery training program to help to equip drivers with the skills to recognize and recover safely from this dangerous and often deadly scenario.

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Driver assistance system for lane departure avoidance by steering and differential braking

Cited by 16 publications

References 4 publications

Realization and Evaluation of an Instructor-Like Assistance System for Collision Avoidance

Realization and Evaluation of an Instructor-Like Assistance System for Collision Avoidance

Recognizing Driver Braking Intention with Vehicle Data Using Unsupervised Learning Methods

Validation of a fixed-base automotive simulator for run-off-road safety and recovery training

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