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Sensor-Fused Nighttime System for Enhanced Pedestrian Detection in ADAS and Autonomous Vehicles.

Jungme Park¹, Bharath Kumar Thota¹, Karthik Somashekar¹

¹College of Engineering, Kettering University, Flint, MI 48504, USA.

Sensors (Basel, Switzerland)

|July 27, 2024

Summary

This study introduces a sensor-fused system for nighttime pedestrian detection, combining thermal and RGB cameras. The developed Deep Neural Network (DNN) model significantly improves detection accuracy and speed for safer autonomous driving.

Keywords:

ADAS Deep Neural Network embedded devices image alignment nighttime object detection sensor-fusion transfer learning

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Area of Science:

Computer Vision
Robotics
Automotive Engineering

Background:

Nighttime road safety is challenged by poor visibility, increasing the risk for vulnerable road users.
Existing environmental perception systems struggle with accurate and timely detection of pedestrians in low-light conditions.

Purpose of the Study:

To develop and evaluate a sensor-fused nighttime environmental perception system for enhanced vulnerable road user detection.
To improve the accuracy and reduce the latency of pedestrian detection systems for Advanced Driver Assistance Systems (ADASs) and autonomous vehicles.

Main Methods:

Integration of thermal and RGB camera data using a novel alignment algorithm for effective sensor fusion.
Development of Deep Neural Network (DNN) models, including three fusion techniques and two single-sensor models, trained on a dataset of 32,000 nighttime image pairs.
Optimization of the best-performing late-fusion model for real-time inferencing on embedded edge computing devices.

Main Results:

Sensor-fused models demonstrated superior performance compared to single-sensor (RGB or thermal) models.
The optimized late-fusion model achieved 33 frames per second (fps) on an embedded device, an 83.33% improvement in inference speed.
The system achieved a balance between high accuracy and rapid response time for nighttime pedestrian detection.

Conclusions:

Sensor fusion of thermal and RGB data significantly enhances nighttime environmental perception systems.
The developed DNN system offers a viable solution for real-time, accurate pedestrian detection, crucial for ADASs and autonomous driving.
This research contributes to improving road safety by reducing nighttime accidents involving vulnerable road users.