Driver steering assistance for lane departure avoidance

Enache, Nicoleta Minoiu; Netto, Mariana; Mammar, Saïd; Lusetti, Benoît

doi:10.1016/j.conengprac.2008.10.012

Cited by 121 publications

(34 citation statements)

References 16 publications

(12 reference statements)

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“…The authors in [14,15] have proposed control strategies that keep the driver in the loop, while [16] switches between the driver and automata when the assistance system intervenes. Other sophisticated strategies can be used for the design of driver assistance, but they are beyond the scope of this work, since it focuses on the control synthesis and its performance.…”

Section: Activation Strategymentioning

confidence: 99%

Piecewise affine control for lane departure avoidance

Benine-Neto

Mammar

Lusetti

et al. 2013

Vehicle System Dynamics

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International audienceThis article presents the design of a lane departure avoidance system which is conceived to operate even in demanding manoeuvres with respect to the lateral vehicle dynamics. Piecewise affine state feedback and output feedback controllers are used to handle the nonlinear behaviour of the lateral tyre forces. The controllers are designed based on the search of a piecewise quadratic Lyapunov function casted as a bilinear matrix inequalities problem. Experimental tests demonstrate the performance of the controller in degraded road conditions

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Section: Activation Strategymentioning

confidence: 99%

Piecewise affine control for lane departure avoidance

Benine-Neto

Mammar

Lusetti

et al. 2013

Vehicle System Dynamics

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“…Davoudi [35] also seeks to maintain stability through brake, steering, and suspension control. Looking forward, steering predictions [36], steering control [37], and path fitting [38] could all be included in the IS currently being developed. These works show that stability control by means of brake, throttle, and steering control is possible and lend credence to the viability of the proposed IS.…”

Section: Vehicle Safety and Driver Assistancementioning

confidence: 99%

Location-Aware Adaptive Vehicle Dynamics System: Linear Chassis Predictions

Bandy

Cho

Ferris

et al. 2014

Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Sys

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One seminal question that faces a vehicle's driver (either human or computer) is predicting the capability of the vehicle as it encounters upcoming terrain. A LocationAware Adaptive Vehicle Dynamics (LAAVD) System is being developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. In contrast to current active safety systems, this system is predictive, not reactive. The LAAVD System employs a predictor-corrector method in which the driver's input commands (throttle, brake, steering) and upcoming driving environment (terrain, traffic, weather) are predicted. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin (PM), to assess the need to intervene. The driver's throttle and brake control are modulated to affect desired changes to the PM in a manner that is minimally intrusive to the driver's control authority. This system depends heavily on an understanding of the interplay between the vehicle's longitudinal, lateral, and vertical forces, as well as their resulting moments. These vehicle dynamics impact the PM metric and ultimately the point at which the Intervention Strategy will modulate the throttle and brake controls. Real-time implementation requires the development of computationally efficient predictive models of the vehicle dynamics.In this work, a method for predicting future vehicle states, based on current states and upcoming terrain, is developed using perturbation theory. An analytical relationship between the change in the spindle forces and the resulting change in the PM is derived, and the inverse relationship, between change in PM and resulting changes in longitudinal forces, is modeled. This model is implemented in the predictor-corrector algorithm of the Intervention Strategy. Corrections to the predicted states are made at each time step using a detailed, full, non-linear vehicle model. This model is run in real-time and is intended to be replaced with a drive-by-wire vehicle. Finally, the impact of this work on the automotive industry is discussed and recommendations for future work are given.iii DedicationTo my parents, for teaching me that life is a journey, not a race.iv

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“…Automatic steering control of vehicles has been investigated for both autonomous vehicles (AV) [1][2][3] and driver steering assistance [4,5]. Driver steering assistance systems under development include collision avoidance systems, adaptive cruise control, and lane departure avoidance systems.…”

Section: Introductionmentioning

confidence: 99%

Empirical Modeling of Steering System for Autonomous Vehicles

Kim¹,

Min²,

Kim³

2017

Journal of Electrical Engineering and Technology

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-To design an automatic steering controller with high performance for autonomous vehicle, it is necessary to have a precise model of the lateral dynamics with respect to the steering command input. This paper presents an empirical modeling of the steering system for an autonomous vehicle. The steering system here is represented by three individual transfer function models: a steering wheel actuator model from the steering command input to the steering angle of the shaft, a dynamic model between the steering angle and the yaw rate of the vehicle, and a dynamic model between the steering command and the lateral deviation of vehicle. These models are identified using frequency response data. Experiments were performed using a real vehicle. It is shown that the resulting identified models have been well fitted to the experimental data.

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Driver steering assistance for lane departure avoidance

Cited by 121 publications

References 16 publications

Piecewise affine control for lane departure avoidance

Piecewise affine control for lane departure avoidance

Location-Aware Adaptive Vehicle Dynamics System: Linear Chassis Predictions

Empirical Modeling of Steering System for Autonomous Vehicles

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