Maintaining Safe and Robust Human-Machine Interaction in Autonomous Electric Vehicles: State-of-the-Art Techniques

Abstract

Autonomous electric vehicles (AEVs) are gaining popularity due to their potential to reduce accidents caused by human error and decrease carbon emissions. However, ensuring safe and robust human-machine interaction in AEVs remains a significant challenge. To address this challenge, we reviewed several state-of-the-art techniques currently being developed and implemented. Our findings show that AEVs rely on a range of sensors and perception systems, including cameras, lidars, radars, and GPS, to detect and respond to their environment. Advanced perception algorithms and machine learning techniques are used to process the data collected by these sensors and provide real-time information about the vehicle's surroundings. The human-machine interface (HMI) is the primary means of interaction between the vehicle and the passenger, and it should be designed to be intuitive, informative, and easy to use. Artificial intelligence and machine learning algorithms are used to make decisions and adapt to changing road conditions. Cybersecurity measures, such as encryption, authentication, and intrusion detection, are essential to prevent cyberattacks on AEVs. Redundancy and fail-safe systems, including redundant sensors, processors, communication systems, backup power sources, and emergency braking systems, ensure that AEVs can continue to operate safely in the event of a failure or malfunction. Finally, rigorous testing and validation are necessary to ensure that AEVs meet safety standards and perform as intended. Our review provides valuable insights into the state-of-the-art techniques for ensuring robust and safe human-machine interaction in AEVs, which can guide future research and development in this area.