Shared control for lane departure prevention based on the safe envelope of steering wheel angle

Tan, Dan; Chen, Wuwei; Wang, Hongbo; Gao, Zhengang

doi:10.1016/j.conengprac.2017.04.010

Cited by 53 publications

(24 citation statements)

References 20 publications

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“…Local state feedback controllers for 5 subsystems of the driver-vehicle-road T-S fuzzy model from Eqs. (12) and (13) are designed via parallel distributed compensation (PDC).…”

Section: Design Of the H ∞ Controller Based On The T-s Modelmentioning

confidence: 99%

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Parallel Distributed Compensation /H∞ Control of Lane-keeping System Based on the Takagi-Sugeno Fuzzy Model

Chen

Zhao

Wang

et al. 2020

Chin. J. Mech. Eng.

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Current research on lane-keeping systems ignores the effect of the driver and external resistance on the accuracy of tracking the lane centerline. To reduce the lateral deviation of the vehicle, a lane-keeping control method based on the fuzzy Takagi-Sugeno (T-S) model is proposed. The method adopts a driver model based on near and far visual angles, and a driver-road-vehicle closed-loop model based on longitudinal nonlinear velocity variation, obtaining the expected assist torque with a robust H ∞ controller which is designed based on parallel distributed compensation and linear matrix inequality. Considering the external influences of tire adhesion and aligning torque when the vehicle is steering, a feedforward compensation control is designed. The electric power steering system is adopted as the actuator for lane-keeping, and active steering redressing is realized by a control motor. Simulation results based on Carsim/Simulink and real vehicle test results demonstrate that the method helps to maintain the vehicle in the lane centerline and ensures driving safety.

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“…Local state feedback controllers for 5 subsystems of the driver-vehicle-road T-S fuzzy model from Eqs. (12) and (13) are designed via parallel distributed compensation (PDC).…”

Section: Design Of the H ∞ Controller Based On The T-s Modelmentioning

confidence: 99%

“…The predictive control model provides a method for danger estimation and decision and optimal control, it is thus used in lane-keeping assisted systems by many researchers [13,14]. Many controllers are designed using nonlinear vehicle models or simplified linear models.…”

Section: Introductionmentioning

confidence: 99%

Parallel Distributed Compensation /H∞ Control of Lane-keeping System Based on the Takagi-Sugeno Fuzzy Model

Chen

Zhao

Wang

et al. 2020

Chin. J. Mech. Eng.

Self Cite

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“…In this scenario, the distraction task starts at about 52 s (Point A in Figure 6) and lasts for 8 s, which is a relatively long time. At 52 s, the TLC is about 14 s, at which point there is no driving risk and no assistance is required according to previous studies [8]. If the cooperation system is only designed considering the driver's distraction, the automatic driving system is activated too early and may generate an intervention to the driver.…”

Section: Accuracy Of Dd Based Detection Strategymentioning

confidence: 99%

“…However, due to the limitation of current technologies and product costs, the driverless car running in public traffic is still under research and humans will be in the driving loop for a long time [5,6]. Hence, the human-machine cooperative driving, which allows both machines and humans to pilot concurrently, has drawn much attention recently [7,8].…”

Section: Introductionmentioning

confidence: 99%

Detection of Driving Capability Degradation for Human-Machine Cooperative Driving

Gao

2020

Sensors

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Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.

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“…ADAS control to prevent ROR crashes has been proposed previously, see for example [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]. In existing literature, the common approach is to assume an incompetent driver and provide warning, overriding control or shared control to trigger a crash-avoiding intervention.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of Vehicle Dynamics for the Prevention of Road Departure on Curved Roads

Gao

Gordon

2019

IEEE Trans. Veh. Technol.

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Runoff Road crashes are often associated with excessive speed in curves, which may happen when a driver is distracted or fails to compensate for reduced surface friction. This work introduces an Automated Emergency Cornering (AEC) system to protect against the major effects of over-speeding on curves, especially lateral deviation leading to lane or road departure. The AEC architecture has two levels: an upper level to perform motion planning, based on the optimal control of a nonlinear particle model, and a lower level to distribute the resulting two-dimensional acceleration reference to the available actuators. The lower level adopts the recently introduced Modified Hamiltonian Algorithm (MHA), which continuously adjusts the priority between mass-centre acceleration and yaw moment demands derived from lateral stability targets. AEC makes use of a high precision map and triggers control interventions based on vehicle kinematic states and detailed road geometry. To avoid false-positive interventions, AEC is triggered only when excessive road departure is predicted for the optimal particle motion. AEC then takes control of steering and individual wheel brake actuators to perform autonomous motion control for speed and path curvature at the limits of available friction. The AEC system is tested and evaluated using the high-fidelity simulation software CarMaker.

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Shared control for lane departure prevention based on the safe envelope of steering wheel angle

Cited by 53 publications

References 20 publications

Parallel Distributed Compensation /H∞ Control of Lane-keeping System Based on the Takagi-Sugeno Fuzzy Model

Parallel Distributed Compensation /H∞ Control of Lane-keeping System Based on the Takagi-Sugeno Fuzzy Model

Detection of Driving Capability Degradation for Human-Machine Cooperative Driving

Optimal Control of Vehicle Dynamics for the Prevention of Road Departure on Curved Roads

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