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.

Much research has gone into developing advanced control algorithms for semi-active suspension. Experimental validation of these control algorithms is critical for their practical applications. This paper investigates a state-observer-based Takagi-Sugeno fuzzy controller (SOTSFC) design for a semi-active quarter-car suspension installed with a magnetorheological (MR) damper and provides proof of the effectiveness of the proposed controller. To conduct the test, a quarter-car test rig and control system hardware were used. Then, a new MR damper was designed and built to fit with the test rig. After that, the SOTSFC for the quarter-car test rig was developed. Finally, several tests were conducted on the quarter-car suspension in order to investigate the real effect of the SOTSFC. It was then compared with the use of a skyhook controller to demonstrate its benefits.

This paper reports an experimental setup aiming at evaluating the performance of a newly designed magnetorheological elastomer (MRE) seismic isolator. As a further effort to explore the field-dependent stiffness/damping properties of the MRE isolator, a series of experimental testing were conducted. Based upon the analysis of the experimental responses and the characteristics of the MRE isolator, a new model that is capable of reproducing the unique MRE isolator dynamics behaviors is proposed. The validation results verify the model’s effectiveness to portray the MRE isolator. A study on the field-dependent parameters is then provided to make the model valid with fluctuating magnetic fields. To fully explore the mechanism of the proposed model, an investigation relating the dependence of the proposed model on every parameter is carried out.

Early detection of driver mental fatigue is one of the active areas of research in smart and intelligent vehicles. There are various methods, based on measuring the physiological characteristics of the driver utilising sensors and computer vision, proposed in the literature. In general, driver behaviour is unpredictable that can suddenly change the nature of driving and dynamics under mental fatigue. This results in sudden variations in driver body posture and head movement, with consequent inattentive behaviour that can end in fatal accidents and crashes. In the process of delineating the different driving patterns of driver states while active or influenced by mental fatigue, this paper contributes to advancing direct measurement approaches. In the novel approach proposed in this paper, driver mental fatigue and drowsiness are measured by monitoring driver’s head posture motions using XSENS motion capture system. The experiments were conducted on 15 healthy subjects on a MATHWORKS driver-in-loop (DIL) simulator, interfaced with Unreal Engine 4 studio. A new modified bidirectional long short-term memory deep neural network, based on a rectified linear unit layer, was designed, trained and tested on 3D time-series head angular acceleration data for sequence-to-sequence classification. The results showed that the proposed classifier outperformed state-of-art approaches and conventional machine learning tools, and successfully recognised driver’s active, fatigue and transition states, with overall training accuracy of 99.2%, sensitivity of 97.54%, precision and F1 scores of 97.38% and 97.46%, respectively. The limitations of the current work and directions for future work are also explored.

The Steer-by-Wire (SbW) system is an electronically controlled steering system that is able to improve steering capability without mechanical links between the steering wheel and the front wheels. However, failure of the SbW system actuator may lead to steering performance degradation and result in instability. In this paper, a fault tolerant sliding mode predictive control (SMPC) strategy for an SbW system is proposed. The sliding mode control is applied to improve the robustness of the model predictive control (MPC) in the presence of modeling uncertainties and disturbances, while the MPC is applied to enhance the fault tolerant capability of the steering control processes. The chaos particle swarm optimization (CPSO) algorithm is introduced to optimize the MPC and a two-stage Kalman filter is introduced to simultaneously provide fault information and state estimation. The performance of the proposed approach is validated through computer simulation. The results demonstrate that the proposed SMPC-CPSO controller is more robust and provides better tracking performance in the presence of model uncertainties, disturbance, and actuator faults than SCMP-PSOs (heterogeneous comprehensive learning particle swarm optimization, evolutionary particle swarm optimizer, etc), SMPC-differential evolution, MPC, SMPC, and MPC-PSO.