The increasing use of active front steering (AFS) technology for obstacle avoidance raises the question of drivers' interaction with vehicle automation. Mathematical models capable of representing ...such interaction are in demand for driver behavior study. This paper presents the application of open-loop Stackelberg equilibrium to modeling a driver's interaction with vehicle AFS control in an obstacle avoidance scenario, where both the driver and the AFS controller are exerting steering control to the vehicle. In this paper, such driver-AFS interactive steering control is modeled as a leader-follower game. Mathematical expressions of the driver's and the AFS controller's steering control strategies are derived using the linear quadratic dynamic optimization approach and the distributed model predictive control (DMPC) approach. These two approaches are found to give identical control gains, which suggest their equivalence in representing driver-AFS interaction. The DMPC approach is found to consume far less computation time due to its numerical nature. Mathematical modifications to the steering control strategies are then introduced to allow practical implementation for a future experimental study. Simulation results including time histories of steering angles and vehicle responses are illustrated and discussed.
Development of vehicle active steering collision avoidance systems calls for mathematical models capable of predicting a human driver's response so as to reduce the cost involved in field tests while ...accelerating product development. This paper provides a discussion on the paradigms that may be used for modeling a driver's steering interaction with vehicle collision avoidance control in path-following scenarios. Four paradigms, namely decentralized, noncooperative Nash, noncooperative Stackelberg, and cooperative Pareto are established. The decentralized paradigm, which is developed on the basis of optimal control theory, represents a driver's interaction with the collision avoidance controllers that disregard driver steering control. The noncooperative Nash and Stackelberg paradigms are used for predicting a driver's steering behavior in response to the collision avoidance control that actively compensates for driver steering action. These two are devised based on the principles of equilibria in noncooperative game theory. The cooperative Pareto paradigm is derived from cooperative game theory to model a driver's interaction with the collision avoidance systems that take into account the driver's target path. The driver and the collision avoidance controllers' optimization problems and their resulting steering strategies arise in each paradigm are delineated. Two mathematical approaches applicable to these optimization problems namely the distributed model predictive control and the linear quadratic dynamic optimization approaches are described in detail. A case study illustrating a conflict in steering control between driver and vehicle collision avoidance system is performed via simulation. It was found that the variation of driver path-error cost function weights results in a variety of steering behaviors, which are distinct between paradigms.
Briefing: In the construction of Metaverses, sensors that are referred to as the "bridge of information transmission", play a key role. The functionality and efficiency of today's sensors, which ...operate in a manner similar to physical sensing, are frequently constrained by their hardware and software. In this research, we proposed the Parallel Sensing framework, which includes background, concept, basic methods and typical application of parallel sensing. In our formulation, sensors are redefined as the integration of real physical sensors and virtual software-defined sensors based on parallel intelligence, in order to boost the performance of the sensors. Each sensor will have a parallel counterpart in the virtual world within the framework of parallel sensing. Digital sensors serve as the brain of sensors and maintain the same properties as physical sensors. Parallel sensing allows physical sensors to operate in discrete time periods to conserve energy, while cloud-based descriptive, predictive, and prescriptive sensors operate continuously to offer compensation data and serve as guardians. To better illustrate parallel sensing concept, we show some example applications of parallel sensing such as parallel vision, parallel point cloud and parallel light fields, both of which are designed by construct virtual sensors to extend small real data to virtual big data and then boost the performance of perception models. Experimental results demonstrate the effective of parallel sensing framework. The interaction between the real and virtual worlds enables sensors to operate actively, allowing them to intelligently adapt to various scenarios and ultimately attain the goal of "Cognitive, Parallel, Crypto, Federated, Social and Ecologic" 6S sensing.
The challenging issue of "human-machine copilot" opens up a new frontier to enhancing driving safety. However, driver-machine conflicts and uncertain driver/external disturbances are significant ...problems in cooperative steering systems, which degrade the system's path-tracking ability and reduce driving safety. This paper proposes a novel stochastic game-based shared control framework to model the steering torque interaction between the driver and the intelligent electric power steering (IEPS) system. A six-order driver-vehicle dynamic system, including driver/external uncertainty, is established for path-tracking. Then, the affine linear-quadratic-based path-tracking problem is proposed to model the maneuvers of the driver and IEPS. Particularly, the feedback Nash and Stackelberg frameworks to the affine-quadratic problem are derived by stochastic dynamic programming. Two cases of copilot lane change driving scenarios are studied via computer simulation. The intrinsic relation between the stochastic Nash and Stackelberg strategies is investigated based on the results. And the steering-in-the-loop experiment reveals the potential of the proposed shared control framework in handling driver-IEPS conflicts and uncertain driver/external turbulence. Finally, the copiloting experiments with a human driver further demonstrate the rationality of the game-based pattern between both the agents.
Stability as well as robustness is the major concerns in the design of a trajectory tracking controller for an autonomous vehicle. In this paper, a novel lateral stability controller design for ...vehicle path tracking is developed. First, using dynamic game theory as a general framework, vehicle lateral stability can be viewed as a dynamic difference game so that its two players, namely, the active front steering (AFS) system and active rear steering (ARS) system can work together to provide more stability for vehicle path tracking control. The interactive steering control strategies between AFS and ARS are obtained by noncooperative closed-loop feedback Stackelberg game theory to ensure optimal performance for vehicle path tracking. Then, based on the proposed path-following shared control paradigm, by applying the method of zero-sum game theory, a finite-time robust regulator is developed to make the interaction model more robust to uncertain lateral disturbances. Finally, double-lane change and serpentine driving condition with and without uncertain time-varying lateral disturbance are used to evaluate the proposed control algorithm. Simulation and hardware-in-loop implementation results show that the proposed shared control paradigm based robust path-tracking controller can robustly provide better lateral stability when time-varying lateral disturbances are bounded.
As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great ...importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg-Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.
In this article, a proportional integral (PI) fault observer is introduced due to its accuracy and expeditiousness. Considering the time-varying vehicle speed and other uncertain parameters in ...vehicle dynamics system, a type-2 fuzzy model is proposed to describe the system nonlinearity. Moreover, it could also address the problem of uncertainty in the membership function, which resulted from the parameter uncertainty. In addition, the <inline-formula><tex-math notation="LaTeX">\mathcal {H}_{\infty }</tex-math></inline-formula> technique is studied to attenuate the effect of disturbance on the estimation performance and a set of linear matrices inequalities are obtained. The particle swarm optimization (PSO) approach is then adopted to find solutions to the PI fault observer. Finally, a simulation test is carried out based on the experimental data, which are collected from an electric vehicle. The simulation results demonstrate the effectiveness of the proposed <inline-formula><tex-math notation="LaTeX">\mathcal {H}_{\infty }/PSO</tex-math></inline-formula> method.
Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by ...specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.
Automated steering technology offers significant benefits to the safety of vehicles, but desire to keep the human driver in the loop requires a better understanding of the interaction between driver ...and vehicle. An existing noncooperative-game-theoretic framework for modelling such interaction is revisited, leading to the development of two driver steering control models. Both bear Nash-equilibrium properties, but involve different assumptions about driver steering behaviour. A simulation study is performed to demonstrate the difference between the two driver models. An experiment using a fixed-base driving simulator is conducted to measure six test subjects' steering angles in response to the lane-change manoeuvres generated by an automated steering controller. The two driver models' capabilities for representing driver steering behaviour are investigated through fitting them to measured driver steering angles. Key model parameters are identified using a system identification procedure. It is found that the two driver models have equivalent capability in capturing the trend of the six test subjects' measured steering angles, but less good at reproducing the overshoot and oscillation involved in two subjects' steering angles. It is found that the inclusion of an arm neuromuscular system model can improve the performance of the proposed driver models.
This is the brief report of the first IEEE Distributed/Decentralized Hybrid Workshop on Future Directions of Intelligent Vehicles (IEEE DHW-FDIV), part of the IEEE Distributed/Decentralized Hybrid ...Symposia on Intelligent Vehicles (IEEE DHS-IV) organized by the IEEE Transactions on Intelligent Vehicles (TIV). This DHW was conducted through two events on January 12 and February 7, 2022 with 23 and 12 participants from Asia, Europe, and North America, respectively. Various issues related to the current state of IEEE TIV and potential topics for future research and development of intelligent vehicles are addressed. Based on the suggestion of Professor Fei-Yue Wang, the new Editor-in-Chief of TIV, the first report of DHW-FDIV focuses on meta-vehicles and metaverses for smart mobility and intelligent transportation.
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