Behavior Modeling of AV Users
Automated vehicles excel on highways but struggle in cities due to pedestrians' and road users' unpredictable, rapidly changing actions.
Last Updated: 06/04/2025 | All information is accurate and up-to-date
Main Goals
- Modeling the behavioral patterns and characteristics of AV users.
- Drivers, occupants, pedestrians and other road users potentially interacting with AVs.
- Guide the design, development and evaluation of human-centric AI functions in intelligent transportation.
Research Examples
Naturalistic Driving Study #1
110-car Naturalistic Driving Study
- Goal: behaviors of pedestrians and bicyclists in a naturalistic road environment when encountering cars.
- Method: Synchronized data from three sources were recorded continuously when the subject was driving through the onboard data collection device hidden behind the rear view mirror.
- Scope:
- Number of primary subjects: 116
- Duration: 12 months
- Collected raw data size:
- Driving distance: ~1.44 million miles
- Driving time: > 40,000 hours
- Video data collected: 90 TB
- Dataset:
- 62,000 pedestrians and 13,000 bicyclists with behavior, environment, and risk labels.
Naturalistic Driving Study #2
- Naturalistic panoramic road-scene data collection
- 200 hours of naturalistic data collection in 4 US Regions
- All VRUs: Pedestrians, E-scooter Riders, Bicyclists
- Global scene reconstructions (LiDAR + 360 Camera + RTK GPS)
- Comprehensive annotations (Visual, Map, Action, and Intention labels)
Data Collection Sites
Experiment Vehicle
Automatic and Manual Annotations of VRU-Encountering Scenes
Visual, movement and action annotations for more than 600k frames to measure the behaviors of VRUs and the contextual scenes.
Scene Reconstruction and Behavior Variable Calculation for >2000 VRUs
| Scenario Variables | eScooter #1 |
|---|---|
| Front eScooter Speed (mph) | 16.04 |
| Ego Speed (mph) | 12.47 |
| Closest Passing Distance (m) | n/a |
| Crossing Distance (m) | 32.12 |
| Crossing Angle (deg) | 61.45 |
| Smallest TTC (sec) | n/a |
Descriptive Vulnerable Road User Behavior Models
Behavior modeling based on naturalistic observations can contribute important details for encountering scenarios.
- Pedestrians and bicyclists moving speeds at different scenarios
- Pedestrian and bicyclist limb motion
- Pedestrian and bicyclist appearance locations
- Calculation of time-to-potential-conflict (TTC)
- Vehicle traveling speed at different scenarios
- 200 hours of naturalistic data collection in 4 US Regions
- All VRUs: Pedestrians, E-scooter Riders, Bicyclists
- Global scene reconstructions (LiDAR + 360 Camera + RTK GPS)
- Comprehensive annotations (Visual, Map, Action, and Intention labels)
E-scooter and cyclist behavior analysis during car encounters based on naturalistic observations.
