Hazard-evaluation-oriented moving horizon parallel steering control for driver-automation collaboration during automated driving

Guo, Hongyan; Song, Linhuan; Li, Jun; Wang, Fei‐Yue; Cao, Dongpu; Chen, Hong; Lv, Chen; Luk, P.C.K.

doi:10.1109/jas.2018.7511225

Cited by 40 publications

(13 citation statements)

References 21 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The continuous prediction of the future steering intention, such as the steering torque, will provide an essential clue to the shared steering control systems. A compensate and optimized steering strategy can be determined by the automation in advance to guarantee driving safety in both typical and critical environments [15]. Second, for the partially automated driving vehicle, the control authority transition between the human driver and the automation should be safe and smooth [16].…”

Section: B Literature Reviewmentioning

confidence: 99%

Hybrid-Learning-Based Driver Steering Intention Prediction Using Neuromuscular Dynamics

Xing

Liu

et al. 2022

IEEE Trans. Ind. Electron.

Self Cite

View full text Add to dashboard Cite

The emerging automated driving technology poses a new challenge to driver-automation collaboration, which requires a mutual understanding between humans and machines through their intention identifications. In this study, oriented by human-machine mutual understanding, a driver steering intention prediction method is proposed to better understand human driver's expectation during driver-vehicle interaction. The steering intention is predicted based on a novel hybrid-learning-based timeseries model with deep learning networks. Two different driving modes, namely, both hands and single right-hand driving modes, are studied. Different electromyography (EMG) signals from the upper limb muscles are collected and used for the steering intention prediction. The relationship between the neuromuscular dynamics and the steering torque is analyzed first. Then, the hybrid-learningbased model is developed to predict both the continuous and discrete steering intentions. The two intention prediction networks share the same temporal pattern exaction layer, which is built with the Bi-directional Recurrent Neural Network (RNN) and Long short-term memory (LSTM) cells. The model prediction performance is evaluated with a varied history and prediction horizon to exploit the model capability further. The experimental data are collected from 21 participants of varied ages and driving experience. The results show that the proposed method can achieve a prediction accuracy of around 95% steering under the two driving modes.

show abstract

Section: B Literature Reviewmentioning

confidence: 99%

Hybrid-Learning-Based Driver Steering Intention Prediction Using Neuromuscular Dynamics

Xing

Liu

et al. 2022

IEEE Trans. Ind. Electron.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to assist the driver to track the desired path while reducing the risk of rollover, a shared control strategy based on fuzzy control method is designed by considering the road hazard level and the driver steering operation hazard, which is enlightened by [27]. Considering the driver's current state and behavior, the following parameters are selected to describe the driver steering operation hazard based on the information available from the current scheme, expressed by: P driver mpc driver     (25) where 0 [28].…”

Section: ) Human-machine Shared Control Strategymentioning

confidence: 99%

Roll Stability and Path Tracking Control Strategy Considering Driver in the Loop

Xuan-yao

2021

IEEE Access

View full text Add to dashboard Cite

A study pointed out that the delay time of the driver's nervous system has a significant effect on the roll stability of the vehicle. However, the existing researches on vehicle rollover prevention control rarely consider the influence of driver factors on vehicle roll stability. Aiming at this problem, a vehicle roll stability and path tracking control strategy considering driver in the loop is proposed to assist different types of drivers. It includes the supervisory decision layer and execution layer. The supervisory decision layer selects the corresponding control mode according to the driver's steering wheel angle change rate, path tracking deviation and vehicle roll stability information. The execution layer includes three modes: human-machine shared steering, active braking and integrated chassis control. The human-machine shared steering and active braking modes assist the driver to improve the roll stability and path tracking accuracy. The integrated chassis control mode is used for the automatic driving of vehicles under emergency conditions. Simulation results show that the proposed control strategy can effectively improve the vehicle roll stability and path tracking accuracy, and reduce the driver's operating burden. INDEX TERMS Roll stability, path tracking, human-machine sharing, coordinated control, integrated control.

show abstract

“…1 can be an automated steering system for collision avoidance, e.g. [5], or an active accelerator pedal for eco-driving, e.g. [19], etc.…”

Section: Scheme For Cooperative-game Interaction Between Human Driver and Automated Driving Systemmentioning

confidence: 99%

“…and are parameters used to specify the relative weights between and . The driver's cost function ( 4) can be then simplified as: (5) where and .…”

Section: ) Construction Of Cost Functionsmentioning

confidence: 99%

“…On detecting the pedestrian, the automated steering system may decide to steer around the pedestrian based on some prescribed hazard evaluation logic, e.g. the one proposed in [5]. However, the driver may decide to decelerate the vehicle and meanwhile keep the vehicle travelling straight ahead since the driver may think the pedestrian will run across the road very quickly.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling of a human driver s interaction with vehicle automated steering using cooperative game theory

Cole

2019

IEEE/CAA J. Autom. Sinica

View full text Add to dashboard Cite

The introduction of automated driving systems raised questions about how the human driver interacts with the automated system. Non-cooperative game theory is increasingly used for modelling and understanding such interaction, while its counterpart, cooperative game theory is rarely discussed for similar applications despite it may be potentially more suitable. This paper describes the modelling of a human driver's steering interaction with an automated steering system using cooperative game theory. The distributed Model Predictive Control approach is adopted to derive the driver's and the automated steering system's strategies in a Pareto equilibrium sense, namely their cooperative Pareto steering strategies. Two separate numerical studies are carried out to study the influence of strategy parameters, and the influence of strategy types on the driver's and the automated system's steering performance. It is found that when a driver interacts with an automated steering system using a cooperative Pareto steering strategy, the driver can improve his/her performance in following a target path through increasing his/her effort in pursuing his/her own interest under the driver-automation cooperative control goal. It is also found that a driver's adoption of cooperative Pareto steering strategy leads to a reinforcement in the driver's steering angle control, compared to the driver's adoption of non-cooperative Nash strategy. This in turn enables the vehicle to return from a lane-change maneuver to straight-line driving swifter.

show abstract

Hazard-evaluation-oriented moving horizon parallel steering control for driver-automation collaboration during automated driving

Cited by 40 publications

References 21 publications

Hybrid-Learning-Based Driver Steering Intention Prediction Using Neuromuscular Dynamics

Hybrid-Learning-Based Driver Steering Intention Prediction Using Neuromuscular Dynamics

Roll Stability and Path Tracking Control Strategy Considering Driver in the Loop

Modelling of a human driver s interaction with vehicle automated steering using cooperative game theory

Contact Info

Product

Resources

About