RLDA-based Braking Intention Detection Algorithm for L3 Autonomous Driving

In L3 autonomous driving, driver needs to keep supervising the road condition and take over the vehicle in time when the system fails. According to related research, the transfer of vehicle control between autonomous driving system and driver is risky. Therefore, it is necessary to design a safety assistance system for the driver's takeover action, so as to improve the safety of L3 autonomous driving. Current research on driving assistance systems can not respond adequately to dangerous situations caused by the driver's behavior and is not satisfactory in terms of response speed. Due to the development of braincomputer interface technology, it is feasible to study the driver's electroencephalogram(EEG) that precedes the moment at which the brake pedal is activated, in order to perform an early detection of emergency braking. However, the signal-to-noise ratio(SNR) of EEG collected by non-invasive EEG analyzer is very low. It is a challenge to extract effective features from EEG signal with low SNR and realize online detection of braking intention. To overcome this problem, this paper proposes an online braking intention detection algorithm based on regularized linear discriminant analysis(RLDA). In offline analysis, our algorithm achieves an accuracy rate of nearly 90%, and the advancement reaches 189ms. In online analysis, the accuracy rate is close to 80%, and the advamcement reaches 140ms.