Recent studies on autonomous vehicles focus on improving driving efficiency and ignore driving comfort. Because acceleration and jerk affect driving comfort, we propose a comfort regenerative braking system (CRBS) that uses artificial neural networks as a vehicle-control strategy for braking conditions. An autonomous vehicle driving comfort is mainly determined by the control algorithm of the vehicle. If the passenger’s comfort is initially predicted based on acceleration and deceleration limits, the control strategy algorithm can be adjusted, which would be helpful to improve ride comfort in autonomous vehicles. We implement numerical analysis of the control strategy, ensuring reduced jerk conditions. In addition, backward propagation was applied to estimate the braking force limits of the regenerative braking systems more accurately. The developed algorithm was verified through the Car Sim and MATLAB/Simulink simulations by comparing them with the conventional braking system. The proposed CRBS offers effective regenerative braking within limits and ensures increased driving comfort to passengers.

The market for electric vehicles is growing rapidly. Among them, the demand for a dual motor type 4 WD (Four -Wheel Driving) system is increasing. In this paper, we present the Torque Matching Strategy (TMS) method to select the optimal torque distribution ratio for dual motors. The TMS controller operates to set the optimal efficiency point by linearizing the drive efficiency combination of the two motors. Driving simulation and testing were performed through five drive cycles in the driver model interworking environment implemented in MATLAB and Carsim. The optimal distribution ratio was derived according to the front and rear gear ratios under the load condition, and the driving was verified by comparing it with the TMS control method. The efficiency was numerically verified by comparing the power loss of the driving motor. It reduced up to 34% in Urban Dynamometer Driving Schedule and up to 56.3% in Highway fuel efficiency test. The effectiveness of the TMS control method was demonstrated through the distribution rate trend based on the operation cycle and power loss.

There have been numerous studies attempting to overcome the limitations of current autonomous driving technologies. However, there is no doubt that it is challenging to promise integrity of safety regarding urban driving scenarios and dynamic driving environments. Among the reported countermeasures to supplement the uncertain behavior of autonomous vehicles, teleoperation of the vehicle has been introduced to deal with the disengagement of autonomous driving. However, teleoperation can lead the vehicle to unforeseen and hazardous situations from the viewpoint of wireless communication stability. In particular, communication delay outliers that severely deviate from the passive communication delay should be highlighted because they could hamper the cognition of the circumstances monitored by the teleoperator, or the control signal could be contaminated regardless of the teleoperator’s intention. In this study, communication delay outliers were detected and classified based on the stochastic approach (passive delays and outliers were estimated as 98.67% and 1.33%, respectively). Results indicate that communication delay outliers can be automatically detected, independently of the real-time quality of wireless communication stability. Moreover, the proposed framework demonstrates resilience against outliers, thereby mitigating potential performance degradation.

Recently, interest and research on autonomous driving technology have been actively conducted. However, proving the safety of autonomous vehicles and commercializing autonomous vehicles remain key challenges. According to a report released by the California Department of Motor Vehicles on self-driving, it is still hard to say that self-driving technology is highly reliable. Until fully autonomous driving is realized, authority transfer to humans is necessary to ensure the safety of autonomous driving. Several technologies, such as teleoperation and haptic-based approaches, are being developed based on human-machine interaction systems. This study deals with teleoperation and presents a way to switch control from autonomous vehicles to remote drivers. However, there are many studies on how to do teleoperation, but not many studies deal with communication delays that occur when switching control. Communication delays inevitably occur when switching control, and potential risks and accidents of the magnitude of the delay cannot be ignored. This study examines compensation for communication latency during remote control attempts and checks the acceptable level of latency for enabling remote operations. In addition, supplemented the safety and reliability of autonomous vehicles through research that reduces the size of communication delays when attempting teleoperation. It is expected to prevent human and material damage in the actual accident situation.

We propose an evaluation metric for communication status that classifies the communication delay as passive, additive, and temporary communication delay and an asynchronous delay compensation Kalman filter with an outlier rejection strategy. Experimental and simulation results demonstrate the kalman filter’s ability to handle both additive and temporal communication delays, contributing to improved safety integrity in line with ISO 26262 requirements. The result shows that the proposed filter can resist not only additive communication delays but also can resist temporal communications delays.