Adaptive Driver-Automation Shared Steering Control via Forearm Surface Electromyography Measurement

Visually-impaired people have difficulties to move around in unknown environments. It is even more challenging to do sports such as running. In this study, a wearable haptic guidance system has been developed that can help visually-impaired people to navigate on lanes of any running track individually without requiring external help. The system uses an RGB-D camera and a microcontroller to detect the lanes and to calculate steering angles for navigation. A skin stretch method is used to transfer the steering angles to the user as haptic feedback around the waist. Two user tests were conducted with four participants (one visually-impaired and three able-bodied volunteers) in order to evaluate the proposed system. In the first test, the participants were asked to identify the given haptic feedback on their waist while standing still. In the second test, the system as a whole was evaluated by asking the participants to run the 400 metres on a lane of a standard outdoor running track with the help of the haptic guidance system. The able-bodied participants were blindfolded during the tests. Number of lane violations, number of directional warnings, and total time of run were recorded. Results show that the participants distinguished steering angles with an accuracy of 83.3 %. The participants were also able to complete the 400 metres with reduced lane violations and directional warnings than the reference runs performed without the haptic guidance. Considering these results, we can conclude that the proposed haptic guidance system is a promising tool for visually-impaired individuals who wants to run on a running track alone.

Autonomous driving has been one of the most interesting technologies in recent years with expectation of solving accidents, pollution, and traffic jams (Jo, Lee, & Kim, 2013; Schrank, Eisele, & Lom...

Advanced driver assistance systems (ADASs) have been developed and have subsequently decreased opportunities for driver skill development because of degraded situational awareness and less activity in the control loop. Beginning drivers must therefore engage in training. The idea has arisen that haptic shared control, which encourages a driver to operate the vehicle properly and with correct timing, should serve as a training system for daily driving. Such a system, however, might degrade performance, comfort, or safety because training aspects might increase the driver workload as a result of error correction feedback. The possibility of lowered usability is not acceptable for ADASs. This study was conducted to develop an assist system that achieves workload reduction and skill improvement. A haptic assistance system that applies guidance torque on the steering wheel was proposed and investigated for backward parking, which is a difficult driving situation. Driving simulator experiments were conducted. Results showed that both support and after-use effects were realized. This study provides knowledge for the development of driving systems using haptic assistant systems for which initial skill influences performance.

Shared Steering Control by Steer-by-Wire System

Active steering control is the key technology to improving vehicle safety and driver comfort, and its performance is largely affected by the uncertainties of human-machine interaction. To this end, this paper proposes a robust cooperative game based active steering control strategy considering the human-machine interaction characteristic to ensure the steering stability of the vehicle. The driver-vehicle system is modeled based on the T-S fuzzy structure to describe the uncertainty of the vehicle’s longitudinal velocity. Then the human-machine shared steering control is represented by a dynamic game process, in which the driver and the active steering control system are regarded as the participants. The cooperative game theory is introduced to decide the interaction control level. Such a design can achieve a better overall performance of agents through modeling the human-machine interaction process. Finally, a robust <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> ∞ compensation control method is proposed to improve the anti-interference performance of the system, thus ensuring vehicle stability. The MATLAB/Carsim joint simulation platform is established to verify the effectiveness of the proposed controller. The results show that the proposed human-machine cooperative control framework can guarantee the dynamic interactive performance while improving the vehicle handling performance. Furthermore, the hardware-in-the-loop (HIL) tests based on the LabVIEW-RT system also prove the feasibility.

The Shared Control (SC) cooperation scheme, where the driver and automated driving system control the vehicle together, has been gaining attention through the years as a promising option to improve road safety. As a result, advanced interaction methods can be investigated to enhance user experience, acceptance, and trust. Under this perspective, not only the development of algorithms and system applications are needed, but it is also essential to evaluate the system with real drivers, assess its impact on road safety, and understand how drivers accept and are willing to use this technology. In this sense, the contribution of this work is to conduct an experimental study to evaluate if a previously developed shared control system can improve overtaking performance on roads with oncoming traffic. The evaluation is performed in a Driver-in-the-Loop (DiL) simulator with 13 real drivers. The system based on SC is compared against a vehicle with conventional SAE-L2 functionalities. The evaluation includes both objective and subjective assessments. Results show that SC proved to be the best solution for assisting the driver during overtaking in terms of safety and acceptance. The SC's longer and smoother control transitions provide benefits to cooperative driving. The System Usability Scale (SUS) and the System Acceptance Scale (SAS) questionnaire show that the SC system was perceived as better in terms of usability, usefulness, and satisfaction.

Shared control has gained considerable attention in the automated vehicle field in recent years, both from a theoretical point of view and also with multiple applications. The development of shared control systems was discussed in a previous review, which presented a taxonomy focused on control algorithms. However, it is still necessary to understand how these systems should be assessed in terms of system performance, driver behavior, cooperation, and road safety. This paper aims to review and classify evaluation methods used in recent studies with real drivers. Results of the present review showed that shared control continues to be of interest to researchers of automated vehicles. The methodology for system evaluation has evolved, with more participants, better testing platforms, and a greater number of comparison baselines. To guide the path toward implementing shared control features in commercial vehicles, this review aims to help researchers to perform relevant evaluation studies in future developments.

Individuals with severe physical impairments have difficulties operating electric wheelchairs (EWs), especially in situations where fine steering abilities are required. Automatic driving partly solves the problem, although excessive reliance on automatic driving is not conducive to maintaining their residual physical functions and may cause more serious diseases in the future. The objective of this study was to develop a shared control system that can be adapted to different environments by completely utilizing the operating ability of the user while maintaining the motivation of the user to drive. The operating characteristics of individuals with severe physical impairments were first analyzed to understand their difficulties when operating EWs. Subsequently, a novel reinforcement learning-based shared control method was proposed to adjust the control weight between the user and the machine to meet the requirements of fully exploiting the operating abilities of the users while assisting them when necessary. Experimental results showed that the proposed shared control system gradually adjusted the control weights between the user and the machine, providing safe operation of the EW while ensuring full use of the control signals from the user. It was also found that the shared control results were deeply affected by the types of users.

To enhance the performance of the brain-actuated robot system, a novel shared controller based on Bayesian approach is proposed for intelligently combining robot automatic control and brain-actuated control, which takes into account the uncertainty of robot perception, action and human control. Based on maximum a posteriori probability (MAP), this method establishes the probabilistic models of human and robot control commands to realize the optimal control of a brain-actuated shared control system. Application on an intelligent Bayesian shared control system based on steady-state visual evoked potential (SSVEP)-based brain machine interface (BMI) is presented for all-time continuous wheelchair navigation task. Moreover, to obtain more accurate brain control commands for shared controller and adapt the proposed system to the uncertainty of electroencephalogram (EEG), a hierarchical brain control mechanism with feedback rule is designed. Experiments have been conducted to verify the proposed system in several scenarios. Eleven subjects participated in our experiments and the results illustrate the effectiveness of the proposed method.

Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, current haptic guidance steering systems demonstrate some deficiencies in assisting lane changing. This study explored a new steering interaction method, including the design and evaluation of an intention-based haptic shared steering system. Such an intention-based method can support both lane keeping and lane changing assistance, by detecting a driver's lane change intention. By using a deep learning-based method to model a driver's decision timing regarding lane crossing, an adaptive gain control method was proposed for realizing a steering control system. An intention consistency method was proposed to detect whether the driver and the system were acting towards the same target trajectories and to accurately capture the driver's intention. A driving simulator experiment was conducted to test the system performance. Participants were required to perform six trials with assistive methods and one trial without assistance. The results demonstrated that the supporting system decreased the lane departure risk in the lane keeping tasks and could support a fast and stable lane changing maneuver.

Recently, vehicle driving devices to support infotainment services are being designed for driver’s convenience. Especially, the automobile industry is developing vehicles that use a joy stick instead of a steering wheel from the concept car. The greatest strength of using a joy stick is that the driver feels less workload and fatigue than when using a steering wheel. However, this field still needs more research and experiments for more accurate results. Therefore, this study evaluated the workload when driving with different devices through a subjective questionnaire and measurement of physiological signals. The reason for using not only a survey but also physiological signals is to support the results of the subjective questionnaire, which are not enough to yield accurate results. There are two different kinds of methods to carry out this research: SWAT (subjective workload assessment technique) for the survey and Biopac equipment for the measurement of physiological signals. A thermal infrared camera is used for capturing the driver workload by measuring the facial skin temperature. Furthermore, a previously established driving simulator, GPS (global positioning system), and the virtual Kyeong-Bu highway are used for the experiment. As a result of the experiment with 13 subjects following both methods (the surveys and the measurement of physiological data), it is confirmed that using a drive stick device causes less workload and fatigue to the drivers than using a steering wheel. In addition, the statistical significance of the results is verified through the SPSS (statistical package for the social sciences) statistical analysis program.

The shared control system has been a key technology framework and trend, with its advantages in overcoming the performance shortage of safety and comfort in automated vehicles. Understanding human drivers' driving capabilities and styles is the key to improving system performance, in particular, the acceptance by and adaption of shared control vehicles to human drivers. In this research, personalized shared control considering drivers' main human factors is proposed. A simulated scenario generation method for human factors was established. Drivers' driving capabilities were defined and evaluated to improve the rationality of the driving authority allocation. Drivers' driving styles were analyzed, characterized, and evaluated in a field test for the intention-aware personalized automated subsystem. A personalized shared control framework is proposed based on the driving capabilities and styles, and its evaluation criteria were established, including driving safety, comfort, and workload. The personalized shared control system was evaluated in a human-in-the-loop simulation platform and a field test based on an automated vehicle. The results show that the proposed system could achieve better performances in terms of different driving capabilities, styles, and complex scenarios than those only driven by human drivers or automated systems.

Ruman-robot interaction is one of the most important topics to resolve critical safety problem for application of robotics technology. In this paper, we investigated a driver-automation shared control for forward collision avoidance while automation failure. To understand the influences of shared control on driver behaviors, a driving simulator experiment was conducted by cooperation with fifteen participants, under different levels of automation authority: manual driving, haptic guidance steering, human-in-the-Ioop and human-out-the-Ioop automated lane tracking. The reaction time and lane change trajectory to avoid a forward collision when facing a critical event were measured, and an after-effect of shared control on lane following performance was evaluated by analysis of time-to-lane crossing during manual driving after the critical event. Finally, a statistical analysis was processed to further indicate human-automation interaction tendencies under different levels of automation authority.

This paper presents a new structure for a driving support designed to compensate for the problems caused by the behaviour of the driver without causing a feeling of unease. This assistance is based on a shared control between the human and an automatic support that computes and applies an assisting torque on the steering wheel. This torque is computed from a representation of the hazards encountered on the road by virtual potentials. However, the equilibrium between the relative influences of the human and the support on the steering wheel are difficult to find and depend upon the situation. This is why this driving support includes a modelization of the driver based on an analysis of several face features using a computer vision algorithm. The goal is to determine whether the driver is drowsy or whether he is paying attention to some specific points in order to adapt the strength of the support. The accuracy of the measurements made on the face features is estimated, and the interest of the proposal as well as the concepts raised by such assistance are studied through simulations.

Before the maturation of vehicle’s self-driving, human-vehicle shared control would be a dominant solution in a certain period. Understanding driver’s maneuver behavior is an important prerequisite for providing drivers with different levels of assistance in the collaborative driving system. This research aims to classify the characteristics of drivers’ maneuver modes and establish a general model of driver steering styles.Firstly, an experiment is designed to collect the behavioral data under a certain circumstance of the drivers. As driving simulating has significant advantages over the real vehicle, for instance, the replicability and stability of the testing scene, this experiment is conducted on a driving simulator platform with six degrees of freedom (6-DOF). 38 participants (21 males, 17 females) with different personalities and driving experiences are required to drive through a U-shaped testing scene. Meanwhile, data such as velocities, lateral deviations, and steering wheel torques are recorded at a frequency of 60Hz. Secondly, in the data processing part, the Principal Component Analysis (PCA) is utilized to extract key features from original data, aiming to reduce redundancy between steering characteristics. Two principal components are calculated to represent the original features. And then, determining the clustering number as three by both Elbow Method and Silhouette Coefficient, three types of driving styles are classified by the K-means cluster. Finally, after the explanation based on the corresponding original data of lateral deviations, steering wheel torques, and its change rate, the drivers’ proficiencies and path tracking abilities are compared, and the three styles are defined as moderate, radical, and conservative types. In the result analyzing, the driver’s path tracking ability is reflected by smaller lateral deviation, a middle steering wheel torque represents higher proficiency, and the change rate of the torque can show the extent of radicalness. The results show that the moderate driver type has high proficiency in vehicle control, who has more direction adjustments and strong path tracking accuracy. The radical type drivers also manipulate the steering wheel a lot, but their routes have relatively violent fluctuations. While the conservative drivers operate the steering wheel carefully, which displays their lack of driving adeptness.This study identifies the specific characteristics of drivers’ steering behaviors and obtains the parametric boundary of driving styles. In further work, the results can be used as a design basis for customizing shared steering controllers for different driver types in collaborative driving. After identifying the driving style by measuring certain steering indexes, a personalized co-drive mode can be confirmed, which makes the driver feel “the vehicle drives like him/herself”, then the human-vehicle trust and driving experience can be greatly improved.