  Although Vehicle-to-Pedestrian (V2P) communication can significantly improve
pedestrian safety at a signalized intersection, this safety is hindered as
pedestrians often do not carry hand-held devices (e.g., Dedicated short-range
communication (DSRC) and 5G enabled cell phone) to communicate with connected
vehicles nearby. To overcome this limitation, in this study, traffic cameras at
a signalized intersection were used to accurately detect and locate pedestrians
via a vision-based deep learning technique to generate safety alerts in
real-time about possible conflicts between vehicles and pedestrians. The
contribution of this paper lies in the development of a system using a
vision-based deep learning model that is able to generate personal safety
messages (PSMs) in real-time (every 100 milliseconds). We develop a pedestrian
alert safety system (PASS) to generate a safety alert of an imminent
pedestrian-vehicle crash using generated PSMs to improve pedestrian safety at a
signalized intersection. Our approach estimates the location and velocity of a
pedestrian more accurately than existing DSRC-enabled pedestrian hand-held
devices. A connected vehicle application, the Pedestrian in Signalized
Crosswalk Warning (PSCW), was developed to evaluate the vision-based PASS.
Numerical analyses show that our vision-based PASS is able to satisfy the
accuracy and latency requirements of pedestrian safety applications in a
connected vehicle environment.
