COVERT: Cognitive Internet of Vulnerable Road Users in Traffic

Details

• Creators:
  Kamat, Vineet R. ; Menassa, Carol C. ; Zhou, Xiaoshan
• Corporate Creators:
  University of Michigan. Department of Civil and Environmental Engineering ; Center for Connected and Automated Transportation (CCAT)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Cognition Computer Networks Crosswalks Data Analysis Electroencephalogram (EEG) Hidden Markov Model Human-Vehicle Interaction Human Powered Vehicles Internet Motor Readiness Pedestrian Road-crossing Traffic V2X Vehicle To Vehicle Communications Vulnerable Road Users

  More +
• Publication/ Report Number:
  110
• DOI:
  https://doi.org/10.7302/27982
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  United States. Department of Transportation. University Transportation Centers (UTC) Program
• Abstract:
  Pedestrians who cross roads often emerge from occlusion (i.e., obstructed views) or abruptly begin crossing from a standstill, frequently leading to unintended collisions with vehicular traffic that result in accidents and interruptions. Existing studies have predominantly relied on external network sensing and observational data to anticipate pedestrian motion. However, these methods are post hoc (reactive) and insufficient when pedestrians are occluded or stationary, reducing the vehicles’ ability to respond in a timely manner. This study addresses these gaps by introducing a novel data stream and analytical framework derived from pedestrians’ wearable electroencephalogram (EEG) signals to predict motor planning in road crossings. Experiments were conducted where participants were embodied in a visual avatar as pedestrians and interacted with varying traffic volumes, marked crosswalks, and traffic signals. To understand how human cognitive modules flexibly interplay with hemispheric asymmetries in functional specialization, we analyzed time-frequency representation and functional connectivity using collected EEG signals and constructed a Gaussian Hidden Markov Model to decompose EEG sequences into cognitive microstate transitions based on posterior probabilistic reasoning. Subsequently, datasets were constructed using a sliding window approach, and motor readiness was predicted using the K-nearest Neighbors algorithm combined with Dynamic Time Warping. Results showed that high-beta oscillations in the frontocentral cortex achieved an Area Under the Curve of 0.91 with approximately a 1-second anticipatory lead window before physical road crossing movement occurred. These preliminary results signify a transformative shift towards pedestrians proactively and automatically signaling their motor intentions to autonomous vehicles within intelligent vehicle-to-everything (V2X) systems. The proposed framework is also adaptable to various human-CAV interactions (e.g., bicyclists in traffic), enabling seamless collaboration in dynamic and connected traffic environments.
• Format:
  PDF
• Funding:
  69A3551747105
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:d348aea38a813046c70682d02b8f9dae4db1d28a5b640409775e7640d1f1e24af7283b4a27905e928b17f912afd1f8ab9be19589b1d08bddd39e29025be4c95f
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88512/dot_88512_DS1.pdf
• File Type:
  [PDF - 5.00 MB ]