Key result
Detection of emergency braking intention using driver's EEG signals from the left hemisphere achieved significant classification accuracies around 80%.
Why the study?
The study investigated the recognition of the intention to perform emergency braking using driver electroencephalographic signals to evaluate the feasibility of early detection in advanced driver-assistance systems.
Does EEG signal analysis detect emergency braking intention compared to normal driving in simulated driving environments?
Does EEG signal analysis detect emergency braking intention compared to normal driving in simulated driving environments?
EEG signals from the left hemisphere can classify emergency braking intention with around 80% accuracy, demonstrating feasibility for integration into advanced driver-assistance systems.
EEG-based braking detection is feasible in simulation; leaves open real-world validation before driver-assistance integration.
This work investigates the recognition of the intention to perform emergency braking from driver's electroencephalographic (EEG) signals. To do so, brain signals and vehicle data were recorded in a simulated driving environment while participants had to drive and to avoid potential collisions by performing emergency braking. To resemble realistic conditions, emergency braking were performed during the presence and absence of stress, workload and fatigue. Brain signals were used to study the classification between emergency braking ntention and normal driving. The results showed significant classification accuracies around 80% using EEG signals from the left hemisphere. On the basic of these results, this work shows the feasibility of incorporating recognizable driver's brain signals into advanced driver-assistance systems to carry out early detection of emergency braking situations.
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Hernández et al. (2019) studied Emergency braking intention detection. Electroencephalographic (EEG) signals vs. Normal driving was evaluated on Classification accuracy between emergency braking intention and normal driving. Detection of emergency braking intention using driver's EEG signals from the left hemisphere achieved significant classification accuracies around 80%.
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