RADIS - Rapid Animal Detection and Identification System for Driver Warning
ML/DL-based Roadside Animal Detection System (RADS) deployed in real-world settings, achieving >97% detection accuracy for wildlife approaching roadways.
Problem Statement
Animal-vehicle collisions pose significant risks to both wildlife and human safety, with millions of animals killed on US roads annually and hundreds of human fatalities. Traditional warning systems are often passive and ineffective.
The challenge was to develop an active, real-time detection system that could identify wildlife approaching roadways and provide immediate warnings to drivers, while operating efficiently in edge computing environments.
Technical Implementation
Hardware Components
- • Optical camera (SCW "Sharpshooter 2.0", 2MP unit)
- • NVIDIA Jetson Nano with Maxwell GPU (128 CUDA cores, 472 GFLOPs)
- • Long-range RF communication infrastructure (XBee emitter/receiver)
- • Combined wind and solar power module
- • 4GB LPDDR4 RAM, 16GB eMMC storage, Linux OS
AI Processing Pipeline
- • YOLO-v5 real-time object detection algorithm
- • Bounding box creation around moving objects
- • Classification as vehicle, human, or animal type
- • Trajectory mapping and speed calculation
- • Non-Maximal Suppression (NMS) and IoU optimization
Research Methodology
System Design
- • Low-power edge computing with NVIDIA Jetson Nano
- • Optical camera system for wildlife detection
- • RF communication for driver warning signs
- • Solar and wind power for sustainable operation
- • Real-time video processing pipeline
AI Implementation
- • YOLO-v5 computer vision model for object detection
- • Custom training on feral horse and vehicle datasets
- • Multi-frame analysis for improved accuracy
- • Speed and direction calculation algorithms
- • Automated driver warning system integration
Results & Performance
Field deployment near Reno, Nevada between September 2021 and June 2025 demonstrated the system's real-world effectiveness. The RADIS platform successfully processed thousands of video frames, providing reliable wildlife detection and driver warnings in challenging environmental conditions.
Model Performance
- • Real-time processing on edge devices
- • Speed and direction calculation for wildlife tracking
- • Multi-frame analysis for improved accuracy
- • Automated driver warning system integration
System Reliability
- • Zero unforced reboots
- • No missed heartbeat frames over 10-month period
- • Automated maintenance cycles (4x daily)
- • Solar and wind power sustainability
Technologies Used
Press Coverage
Conference Presentation
RADIS: Rapid Animal Detection and Identification System for Driver Warning
Authors: Vedant Srinivas, Fraser Shilling
Affiliations: Road Ecology Center, Institute of Transportation Studies, University of California, Davis, USA
Conference: International Conference on Ecology and Transportation (ICOET) 2023
ML/DL-based Roadside Animal Detection System (RADS) deployed in real-world settings, achieving >97% detection accuracy for wildlife approaching roadways. The system combines YOLO-v5 computer vision with edge computing to provide real-time driver warnings and reduce animal-vehicle collisions.
Future Work
Future iterations of RADIS will focus on expanding species detection capabilities, improving power efficiency for longer deployment periods, and integrating with smart city infrastructure for comprehensive traffic safety.
The system architecture is designed to be scalable and adaptable for different geographic regions and wildlife populations, with potential applications in national parks, rural highways, and urban wildlife corridors.