See the conflict before the crosswalk does.

PedRisk estimates the chance of a pedestrian–vehicle conflict from tracked movement and raises a low, medium or high alert ahead of the closest approach. Built on 41 real runs from the FHWA Intersection Safety Challenge.

Replay a real run

Choose a run to play back the tracked vehicle and pedestrians with the model's risk score at each moment.

Each run is scored by a model trained without it. Lanes and crosswalks are fitted to all 41 vehicle and pedestrian tracks and styled after the site's aerial view. It is a schematic, not a surveyed map; signal heads are not shown.

What-if

Adjust the eleven model inputs, or start from a recorded run.

Inputs are cumulative over a run.
Logistic regression
Low
MLP neural network
Low

Data

Source

FHWA Intersection Safety Challenge, Stage 1B validation data. Each run has a ground-truth track every 0.1 s for a vehicle and one to three pedestrians or riders, plus a conflict / no-conflict label.

Labels

Location

FHWA Smart Intersection
TFHRC West Intersection
38.955014, -77.149375
Turner-Fairbank Highway Research Center, McLean, Virginia
One signalized four-way intersection, recorded October 2023 to March 2024.

Feature by label

Road users

Runs containing each type (no conflict / conflict).

System

1 Sense

Observe

Tracked positions of the vehicle and each pedestrian or rider, ten times a second.
2 Interpret

Extract features

Eleven features: separation, speeds, closing rate, time to collision, braking, road users.
3 Predict

Score the risk

Logistic regression and an MLP each output a conflict probability.
4 Act

Alert

Low, medium or high risk with a suggested action.

Features

FeatureMeaning

Logistic regression weights

Standardized coefficients. Right of center raises the conflict probability.

Results

80% train / 10% validation / 10% test by run, rotated over all 41 runs and repeated with five shuffles (mean ± sd). Only 12 runs are conflicts, so read these as a proof of concept.

ModelPrecisionRecallF1AccuracyAUC

Distance rule: alert when the closest separation is under a threshold learned from the training runs.

Confusion matrices

Warning lead time

Downloads

Python code

pedrisk_ml.py (model and evaluation)
build_site.py (site data)

Ground-truth tracks

Full sensor data: FHWA on data.gov.