EEG potentials predict upcoming emergency brakings during simulated driving

A large number of car crashes can potentially be prevented with emergency braking assistance. State-of-the-art braking systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash, and if further brake pedal activity is detected, the system automatically performs emergency braking. This paper presents the results of a driving simulator study which indicates that the driver's intention to perform emergency braking can be detected based on muscle activation and cerebral activity prior to the behavioural response. Identical levels of predictive accuracy were attained using electroencephalography (EEG), which worked more quickly then electromyography (EMG), which worked more quickly than pedal dynamics. A simulated assistance system using EEG and EMG was found to detect emergency brakings 130 ms earlier than a system relying only on pedal responses, which may have a significant impact on accident prevention. The results motivate a neuroergonomic approach to driving assistance. The EEG analysis yielded a characteristic event-related potential signature that comprised components related to the sensory registration of a critical traffic situation, mental evaluation of the sensory percept and motor preparation. While all these components should occur often during normal driving, the authors conjecture that it is their characteristic spatio-temporal superposition in emergency braking situations that leads to the considerable prediction performance observed in the study.

• Record URL:
  • http://iopscience.iop.org/1741-2552/8/5/056001/pdf/1741-2552_8_5_056001.pdf
• 
• Authors:
  • Haufe, Stefan
  • Treder, Matthias S
  • Gugler, Manfred F
  • Sagebaum, Max
  • Curio, Gabriel
  • Blankertz, Benjamin
• Publication Date: 2011-10

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: pp 1-11
• Serial:
  • Journal of Neural Engineering
  • Volume: 8
  • Issue Number: 5
  • Publisher: IOP Publishing

Subject/Index Terms

• TRT Terms: Automatic braking; Crash avoidance systems; Driver support systems; Driver vehicle interfaces; Driving simulators; Electroencephalography; Human factors engineering; Safety equipment; Technological innovations
• Uncontrolled Terms: Electromyography
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment; I83: Accidents and the Human Factor; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01352652
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 27 2011 8:16AM