Driver Assisted Lane Keeping with Conflict Management Using Robust Sliding Mode Controller

Abstract

Lane-keeping assistance design for road vehicles is a multi-objective design problem that needs to simultaneously maintain lane tracking, ensure driver comfort, provide vehicle stability, and minimize conflict between the driver and the autonomous controller. In this work, a cooperative control strategy is proposed for lane-keeping keeping by integrating driving monitoring, variable level of assistance allocation, and human-in-the-loop control. In the first stage, a time-varying physical driver loading pattern is identified based on a relationship between lateral acceleration, road curvature, and the measured maximum driver torque. Together with the monitored driver state that indicates driver mental loading, an adaptive driver activity function is then formulated that replicates the levels of assistance required for the driver in the next stage. To smoothly transition authority between various modes (from manual to autonomous and vice versa) based on the generated levels of assistance, a novel higher-order sliding mode controller is proposed and closed-loop stability is established. Further, a novel sharing parameter (which is proportional to the torques coming from the driver and from the autonomous controller) is used to minimize the conflict. Experimental results on the SHERPA high-fidelity vehicle simulator show the real-time implementation feasibility. Extensive experimental results provided on the Satory test track show improvement in cooperative driving quality by 9.4%, reduction in steering workload by 86.13%, and reduced conflict by 65.38% when compared with the existing design (no sharing parameter). These results on the cooperative performance highlight the significance of the proposed controller for various road transportation challenges.

Driving through the Mind: Investigating Driver Cognitive Impairment with Physiological Measures of Mental Workload

“…The research examined the acceptance of advanced safety features in Toyota Sienna and Prius models among 183 vehicle owners through telephone interviews, revealing a high level of interest in adaptive cruise control and forward collision avoidance, but slightly lower acceptance for lane departure warning and prevention, with variations observed based on drivers' age and gender, indicating potential differences in responses to emerging crash avoidance technologies in various vehicle models [6].…”

Virtual Sensor: Simultaneous State and Input Estimation for Nonlinear Interconnected Ground Vehicle System Dynamics

“…All of these ADASs can be enhanced using real-time knowledge of the vehicle state evolution and unknown inputs, such as driver actions and road attributes. For example, vehicle stability and shared steering control systems require side slip angle and driver torque information for their control purposes [1][2][3][4], and future steer-by-wire systems, in which the mechanical steering of today will be replaced with either electrical, hydraulic, or electro-hydraulic steering, will require such information for full-state feedback shared control [5,6]. However, the vehicle state, as well as the inputs, are not all directly measurable: necessary sensors do not exist yet or are still too expensive for use in commercial vehicles.…”

Advanced Sensing and Safety Control for Connected and Automated Vehicles

“…This Special Issue on Sensors aims to report on some of the recent research efforts on this increasingly important topic. The 11 accepted papers in this Issue cover vehicle position estimation [ 1 ], 3D Object Detection [ 2 ], pedestrian state sense [ 3 ], trajectory prediction [ 4 ], criticality assessment [ 5 ], active fault-tolerant control for actuator failure [ 6 ], decision-making in the scenario of highway driving out of the ramp [ 7 ] and uncertain interactive traffic scenarios [ 8 ], RRT-based path planning algorithm [ 9 ], C/GMRES motion planning algorithm [ 10 ], and lane-keeping controller design [ 11 ]. In this introduction, a brief description of the content of each contribution forming the Special Issue is provided.…”

“…Ref. [ 11 ] proposes a cooperative control strategy for lane-keeping by integrating driving monitoring, a variable level of assistance allocation, and human-in-the-loop control. The relationship between lateral acceleration, road curvature, and the observed maximum driver torque is used in the first stage of this research to identify a time-varying physical driver loading pattern.…”

“…In summary, there is a huge potential for the application of CAV in areas of traffic perception, decision-making, and driving safety improvement. This Special Issue contributes to this by proposing solutions to the general problems of state estimation [ 1 ], objective detection [ 2 , 3 ], trajectory prediction [ 4 ], driving safety improvement [ 5 , 6 ], decision-making [ 7 , 8 ], path planning [ 9 , 10 ] and vehicle motion control [ 11 ], which largely represent the theoretical challenges and practical interest of this research topic. Finally, we wish to thank the authors, reviewers, and journal staff for their commitment and effort, without which we could not have completed this Special Issue on time.…”