Human-machine Shared Control Research Articles

Interactive strategy of shared control vehicle and automated vehicles based on adaptive non-cooperative game

Considering the complex traffic environment in the future, an interactive strategy between human-machine shared control vehicle (HSCV) and automated vehicles (AVs) based on the adaptive non-cooperative game (NCG) theory is proposed to ensure the safety of vehicles interaction control involving drivers with different characteristics. In HSCV, first, considering the environmental risk potential field (ERPF) and the human-machine conflict coefficient, an adaptive weight distribution method is designed to realize the weight distribution between a driver and an automation system. Subsequently, the steering angle command imposed by a driver is introduced into the game solving process between HSCV and AVs, enabling adaptive dynamic game through weight adjustment. During V2V interaction, based on NCG and distributed model predictive control, a motion planning controller (MOPC) is designed to integrate the optimal solution problem of path planning and tracking control and solve the interaction problem with constraints. Two typical interactive scenarios are used to verify the feasibility of the strategy. The results demonstrate that the interaction strategy considering the different drivers’ operating commands not only realizes the interaction between a driver and an ego vehicle but also realizes the interaction between an ego vehicle and nearby vehicles to ensure the safety of vehicles.

Human–Machine Shared Control for Path Following Considering Driver Fatigue Characteristics

Human–Machine Shared Control for Path Following Considering Driver Fatigue Characteristics

Dynamic Driving Style Recognition for Human Machine Shared Control

Dynamic Driving Style Recognition for Human Machine Shared Control

Human-Machine Shared Control for Industrial Vehicles: A Personalized Driver Behavior Recognition and Authority Allocation Scheme

Human-Machine Shared Control for Industrial Vehicles: A Personalized Driver Behavior Recognition and Authority Allocation Scheme

Servo-Level Human–Machine Shared Control Flexible Strategy Based on Driving Ability, Status and Regionalized Environmental Risk

The design of interaction strategy between the human driver and Automated Driving System (ADS) brings great challenges to driving automation. A servo-level human-machine shared control flexible strategy (SHSC-FS) is proposed to solve the problem of how to reasonably allocate Driving Control Authority (DCA—the proportional distribution of outputs from the driver and ADS when they complete the same dynamic driving task at the same time together). The strategy can realize DCA allocation by evaluating whether the driver’s long-term ability and status can cope with the real-time environmental risk from different directions around. Through the verification test and the analysis of the natural vehicle trajectory dataset, it is found that the regionalized environmental risk faced by human drivers during driving obeys normal distribution. The real-time driving abilities (which is defined as driving status here) of human drivers also obey normal distribution. In addition, the verification tests show that SHSC-FS proposed tends to give higher DCA to the drivers with strong driving ability when they are facing low environmental risk from regions where they are sensitive and good at coping; When dealing with the high environmental risk from regions where drivers are insensitive and not good at dealing with, DCA given to the drivers with weak driving ability is lower.

A Human-Machine Shared Control Framework Considering Time-Varying Driver Characteristics

The uncertainties of driver's behavior seriously affect road safety and bring significant challenges to the human-machine cooperative control. This paper proposes a human-machine shared control framework considering driver's time-varying characteristics to improve the co-driving cooperation performance. Firstly, the driving intention is introduced to describe the driver's involvement level through using Gauss-Bernoulli restricted Boltzmann machine method. And the index of driving ability is proposed to evaluate driver skills based on path-tracking errors. Then, a novel human-machine authority allocation strategy is designed by combining the two driving behavior characteristics and used to construct the driver-vehicle interaction system. Subsequently, a T-S fuzzy robust state-feedback shared control system is developed considering time-varying driver behaviors and vehicle states. Finally, the proposed shared steering system is validated by the driver-in-the-loop test bench. The results show that the proposed control method can reduce human-machine conflicts and has obvious superiority in improving performance of driving comfort, path tracking, and vehicle stability for the co-driving vehicles.

Chat With ChatGPT on Intelligent Vehicles: An IEEE TIV Perspective

This letter reports on a TIV DHW(decentralized and hybrid workshop) that explores the prospective influence of ChatGPT on research and development in intelligent vehicles. To assess the update capabilities of ChatGPT, we conducted tests involving both basic and technically relevant questions. Our preliminary testing revealed that ChatGPT's information can be updated and corrected at one time, but it may take some time for the changes to be reflected in ChatGPT's responses, so it may not always possess the latest knowledge regarding specific topics. We further discuss the prospective influence of ChatGPT on the field of intelligent vehicles, particularly possible applications of ChatGPT in areas like autonomous driving, human-vehicle interaction, and intelligent transportation systems, highlighting challenges and opportunities associated with these applications. Additionally, we address technical questions, such as the feasibility of training intelligent vehicles using the same methods as ChatGPT and the reflection of the intelligence of intelligent vehicles in the context of human-machine shared control. In conclusion, this letter presents a preliminary exploration of the potential of ChatGPT for intelligent vehicle research, from an IEEE TIV perspective, acknowledging the limitations and uncertainties of this emerging technology.

Human-Machine Shared Steering Control for Vehicle Lane Keeping Systems via a Fuzzy Observer-Based Event-Triggered Method

This paper is concerned with the human-machine shared control issue for vehicle lane keeping systems via a new fuzzy observer-based event-triggered method. In order to capture system nonlinear and uncertain characteristics such as nonlinear tire dynamics, varying velocity and driver behavioral uncertainties, Takagi-Sugeno fuzzy approach is employed to model the global driver-vehicle-road system. After system modeling, the fuzzy observer-based control structure is considered because a full states information is not available in practical driving environment. Then, most existing human-machine shared control methods are based on the periodic sampling communication mechanism. However, since the network bandwidth is limited, the above mechanism may cause oversampling and communication congestion. Thus, an adaptive event-triggered mechanism is introduced between the observer and the controller to mitigate the communication burden and improve the bandwidth utilization. Based on Lyapunov functional theory, a set of sufficient conditions are given to calculate desired human-machine shared controllers. Finally, simulation tests are implemented on Matlab/Simulink-CarSim platform and simulation results illustrate that the proposed method can achieve a favorable improvement in the lane keeping capability, the driver handling comfort and the network bandwidth utilization.

Adaptive Steering Torque Coupling Framework Considering Conflict Resolution for Human-Machine Shared Driving

Human-machine shared control has become an effective cutting-edge approach to enhance driving safety and assist in the transition from manual to autonomous driving. However, driving authority allocation when conflict occurs between a human driver and a machine still represents an intractable problem. In order to solve this problem, this paper proposes an adaptive steering torque coupling framework to achieve the human-machine shared control with conflict resolution. Additionally, a driver trajectory prediction method considering human-machine interaction is established based on the extended state observer and long short-term memory network. In addition, the human-machine shared steering system is modeled based on a non-cooperative dynamic game by the prediction model method for path-tracking, and the obtained solution is given according to the Nash equilibrium. Particularly, a dynamic load allocation approach is designed to resolve human-machine conflict and relax the driver. In order to verify the strategy formed based on the proposed framework, a driver-in-the-loop experiment is conducted. The experimental results reveal that the proposed strategy can effectively reduce the human-machine conflict torque and ensure a driver has absolute control authority. Furthermore, the proposed strategy can also transfer the driving workload between the driver and machine, provide driving experience, or improve driving comfort when there is no conflict.

Review on Human-machine Shared Control of Intelligent Vehicles

Review on Human-machine Shared Control of Intelligent Vehicles

Task Learning, Intent Prediction, and Adaptive Blended Shared Control With Application to Excavators

Human operators of construction equipment usually exhibit strong situational awareness, which enables them to execute tasks while handling unexpected uncertainties and adapting to environmental changes. Automatic control, on the other hand, is generally capable of executing repetitive and well-defined tasks more efficiently and precisely. In dynamic and uncertain environments, significant safety and performance benefits can be derived by collaboratively achieving the tasks via the blending of control inputs from the human operator and automatic control. The focus of this article is on developing efficient methods for such blending and its application to hydraulic excavators, which involves the developments in task learning, intent prediction, and human-machine shared control. We propose a new task learning method by segmenting the tasks with the operator primitive-based segmentation (OPbS) and clustering of subgoals via Bayesian nonparametric clustering with temporal ordering (BNPC/TO). We introduce a method for dynamically predicting the operator's intent in seeking a particular subgoal by proposing an empirical stochastic transition matrix (ESTM) and a dynamic angle difference exponential (DADE). We then propose a method for blended shared control with conflict awareness extended from dynamic angle difference. Finally, we apply our algorithms and evaluate the results on a scaled hydraulic excavator test platform for a typical earth-moving task with novice learning operators and a skilled demonstration operator.