Far-Infrared Animal Detection for Vehicle Trajectory Intervention
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Solution Overview
Problem
Existing vehicle collision detection systems struggle to accurately predict the trajectory of large animals in low light and obscured conditions, leading to reduced accuracy and increased computational costs.
Innovation Solution
A large animal intervention system using a far-infrared camera to detect and classify animals, combined with a convolutional long short-term memory attention-based multi-stream encoder-decoder model to predict animal trajectories, and a heads-up display for driver notification, actuating vehicle components as needed to avoid collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If a thermal camera is used to detect animals in poor light conditions, then detection capability is improved, but image accuracy and trajectory prediction reliability deteriorate due to low contrast and noise
Solution Approach 1:
The system segments the detection task into multiple stages: initial animal detection using thermal imaging, classification of detected animals, and trajectory prediction only for classified animals. This segmentation reduces the computational burden and improves accuracy by focusing resources on relevant targets rather than processing all detected objects uniformly.
Solution Approach 2:
Animal classification serves as an intermediary step between detection and trajectory prediction. The system introduces a classification layer that filters detected animals based on species, size, and behavior characteristics, providing more reliable input for trajectory prediction models and improving overall system accuracy.
2Reliability
If trajectory prediction is performed for all detected animals, then completeness of prediction is improved, but computational cost increases significantly
Solution Approach 1:
The system applies partial action by performing trajectory prediction only for a subset of detected animals that meet classification criteria. Rather than predicting trajectories for all detected animals, the system selectively processes high-priority targets, reducing computational cost while maintaining reliability for critical cases.
Solution Approach 2:
Different processing quality levels are applied to different animals based on their classification. High-priority animals receive full trajectory prediction processing, while lower-priority animals receive simplified processing or are excluded from prediction. This local quality approach optimizes computational resource allocation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances collision avoidance by providing accurate animal trajectory predictions, reducing computational costs, and enabling timely driver interventions to prevent vehicle-animal collisions.
Implementation Method 1
A far-infrared camera identifies an image of a large animal in an image frame
Data Source
AI summary
A far-infrared camera mounted in a vehicle generates an image frame. When an image of a large animal is identified in the image frame, a pixel intensity of the large animal image is determined. An estimated distance to the large animal from the far-infrared camera based on the pixel intensity is determined. When the animal is classified as a tracked animal, and future trajectories of the tracked animal and the vehicle intersect, a component in the vehicle is actuated.