A Novel Method of Emergency Situation Detection for a Brain-Controlled Vehicle by Combining EEG Signals With Surrounding Information

Abstract

In this paper, to address the safety of brain-controlled vehicles under emergency situations, we propose a novel method of emergency situation detection by fusing driver electroencephalography (EEG) signals with surrounding information. We first build a novel EEG-based detection model of driver emergency braking intention. We then recognize emergency situations by fusing the result of the proposed EEG-based intention detection model with that of the obstacle detection model based on surrounding information. The real-time detection system of driver emergency braking intention is implemented on an embedded system, and the driver-and-hardware-in-the-loop-experiment of the proposed detection method of emergency situations is performed. Experimental results show that the proposed method can detect emergency situations with the system accuracy of 94.89%, false alarm rate of 0.05%, and response time of 540 ms. This paper has important values in the future development of brain-controlled vehicles, human-centric advanced driver assistant systems, and self-driving vehicles and opens a new avenue on how cognitive neuroscience may be applied to human-machine integration.

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

“…Despite these challenges, several studies have demonstrated the feasibility of BCVs. We can mention, for instance, control of a vehicle in four main directions [107], [108], methods for obstacle avoidance [109], [110], and hand brake assistance in emergency situations [111], [112], using diverse platforms such as vehicle simulators, virtual reality vehicles, vehicles in video games, quadcopters, drones, helicopters, and fixed-wings aircrafts. A general simulator-based procedure for training a participant is shown in Fig.…”

“…In a series of studies by Bi et al [111], [117]- [121], different approaches to identify and predict the driver's intention for Arguably, the major limitation on BCVs is the availability of more informative neural signals than those that EEG or EMG can provide. The use of noninvasive brain signals has led to significant contributions, in particular, better braking systems, but a comprehensive control of a vehicle requires fine-tuned actions.…”

Review of the State-of-the-Art of Brain-Controlled Vehicles

“…The fMRI is usually employed as a hybrid method with the EEG to obtain significant results in real-time systems. [12], [13].…”

Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface

“…EEG data helps to assess the states of the brain and hence EEG signals are commonly used in real-world applications [25,26]. In the EEG-based brain-computer interface (BCI) design, well-recorded data are difficult to collect because most of the subjects are affected by interior and exterior disturbances.…”