We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

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A Prototype SSVEP Based Real Time BCI Gaming System

In this paper, we propose a new steady-state visually evoked potential (SSVEP) brain–computer interface (BCI) with visual stimuli presented on a windshield via a head-up display, and we apply this BCI in conjunction with an alpha rhythm to control a simulated vehicle with a 14-DOF vehicle dynamics model. A linear discriminant analysis classifier is applied to detect the alpha rhythm, which is used to control the starting and stopping of the vehicle. The classification models of the SSVEP BCI with three commands (i.e., turning left, turning right, and going forward) are built by using a support vector machine with frequency domain features. A real-time brain-controlled simulated vehicle is developed and tested by using four participants to perform a driving task online, including vehicle starting and stopping, lane keeping, avoiding obstacles, and curve negotiation. Experimental results show the feasibility of using the human “mind” alone to control a vehicle, at least for some users.

Using a Head-up Display-Based Steady-State Visually Evoked Potential Brain–Computer Interface to Control a Simulated Vehicle

In this paper, we propose a novel driver–vehicle interface for individuals with severe neuromuscular disabilities to use intelligent vehicles by using P300 and steady-state visual evoked potential (SSVEP) brain–computer interfaces (BCIs) to select a destination and test its performance in the laboratory and real driving conditions. The proposed interface consists of two components: the selection component based on a P300 BCI and the confirmation component based on an SSVEP BCI. Furthermore, the accuracy and selection time models of the interface are built to help analyze the performance of the entire system. Experimental results from 16 participants collected in the laboratory and real driving scenarios show that the average accuracy of the system in the real driving conditions is about 99% with an average selection time of about 26 s. More importantly, the proposed system improves the accuracy of destination selection compared with a single P300 BCI-based selection system, particularly for those participants with relatively low level of accuracy in using the P300 BCI. This study not only provides individuals with severe motor disabilities with an interface to use intelligent vehicles and thus improve their mobility, but also facilitates the research on driver–vehicle interface, multimodal interaction, and intelligent vehicles. Furthermore, it opens an avenue on how cognitive neuroscience may be applied to intelligent vehicles.

A Brain–Computer Interface-Based Vehicle Destination Selection System Using P300 and SSVEP Signals

In this paper, we propose a new approach of detecting emergency braking intention for brain-controlled vehicles by interpreting electroencephalography (EEG) signals of drivers. Regularization linear discriminant analysis with spatial-frequency features is applied to build the detection model. These spatial-frequency features are selected from the powers of frequency points across sixteen channels by using the sequential forward floating search. Experimental results from twelve subjects show that on average, the proposed method can detect emergency braking intentions 420 ms after the onset of emergency situations with the system accuracy of over 94%, showing the feasibility of developing a practical system of detecting driver emergency braking intention with the power spectra of EEG signals for brain-controlled vehicles.

EEG-Based Detection of Driver Emergency Braking Intention for Brain-Controlled Vehicles

In this paper, we propose a P300 brain-computer interface (BCI) with visual stimuli presented on a head-up display and we apply this BCI for selecting destinations of a simulated vehicle in a virtual scene. To improve the usability of the selection system, we analyze the effects of the number of electroencephalogram (EEG) rounds on system performance. Experimental results from eight participants show that the BCI-based model of destination selection can be built with EEG data from eight channels, and participants can use this BCI to select a desired destination with an accuracy value of 93.6% ± 1.6% (mean value with standard error) in about 12 s of selection time. This paper lays a foundation for developing vehicles that use a BCI to select a desired destination from a list of predefined destinations and then use an autonomous navigation system to reach the desired destination.

A Head-Up Display-Based P300 Brain–Computer Interface for Destination Selection

The application provides a system and a method of cursor control based on a brain-computer interface. The system comprises a visual evoked simulation module, an electroencephalogram acquisition instrument and a processor, wherein the visual evoked simulation module is used for providing P300 evoked potential stimulation and SSVEP (Steady State Visual Evoked Potential) stimulation for a user in an interface display manner; the electroencephalogram acquisition instrument is used for acquiring the electroencephalogram signal of the user in real time and carrying out amplification and analog-to-digital conversion, and carrying out signal transmission through data wires and the processor; and the processor is used for receiving and processing the electroencephalogram signal, and judging the control purpose of the user and controlling cursor movement.

System and method for cursor control based on brain-computer interface

In this paper, to develop a hybrid brain-computer interface based on Steady State Visual Evoked Potential(SSVEP) and P300, we validate a SSVEP brain-computer interface (BCI) with the hybrid stimuli of SSVEP and P300, and analyze the effect of P300 visual stimuli on the classification accuracy of the SSVEP BCI. The hybrid stimuli include the SSVEP stimuli, which consist of twochessboardimagespresented on a LCD screen and flashing at particular frequencies, respectively, and the P300 stimuli, which include nine characters and locate between the SSVEP stimuli. We extract the fundamental frequencies and second harmonics of the frequencies of the SSVEP stimulifrom EEG data as features, andapply a Fisher's linear discriminant analysis to build the classifier. The experimental results from three subjects show that the SSVEP potentials can be elicited well under the hybrid stimuli of SSVEP and P300, and the P300 stimuli under the hybrid condition have no significant effect on the SSVEP BCI. This work laysa foundationfor developing a hybrid BCIbased on SSVEP and P300.

A SSVEP brain-computer interface with the hybrid stimuli of SSVEP and P300

In this paper, we propose a new Steady State Visual Evoked Potential (SSVEP)-based brain-computer interface (BeI) with the visual stimuli displayed on the windshield via a Head Up Display system. Two chessboard images flickering at specific frequencies and representing different commands were developed as SSVEP stimuli. A linear discriminant analysis classifier was used to recognize the different patterns and output the corresponding commands. Experimental results from three healthy participants with no subject screening being conducted indicate that the average accuracy of the new BeI system is close to 80% with the window length of 4 seconds and step size of 0.25 second, showing that the proposed BeI system is feasible. The potential benefit of this system is that it little obstructs users to look at surroundings while applied to drive a vehicle.

Yun Li

Papers

A Head-Up Display-Based P300 Brain–Computer Interface for Destination Selection

System and method for cursor control based on brain-computer interface

A SSVEP brain-computer interface with the hybrid stimuli of SSVEP and P300

A new SSVEP brain-computer interface based on a head up display

Method controlling vehicle by electroencephalogram and intelligent vehicle using method