Vehicle Environment Classification for Safety System Resource Optimization
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Solution Overview
Problem
Safety systems for motor vehicles face issues with false and missed detections of pedestrians, bicyclists, and animals due to high processing demands, leading to reduced reliability and driver distrust.
Innovation Solution
A classification system that adjusts safety protocols based on the vehicle's environment, deactivating irrelevant algorithms and reallocating processing resources to enhance detection reliability, using a combination of image data and vehicle sensors to categorize environments like city, non-city, and rural areas, and activating/deactivating animal detection accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If detection algorithms are continuously activated to detect all potential subjects (pedestrians, bicyclists, animals), then detection coverage is improved, but processing capacity is exceeded leading to false detections and system overload
Solution Approach 1:
The system dynamically adjusts the activation state of detection algorithms based on the classified environment category. In city environments, animal detection is deactivated while pedestrian detection remains active. In rural environments, both pedestrian and animal detection are activated. This dynamic configuration optimizes processing capacity utilization while maintaining detection reliability for relevant subjects in each environment.
Solution Approach 2:
The system applies different detection algorithm configurations to different environment categories. Instead of using a uniform detection strategy, the system tailors the activation of specific detection algorithms (pedestrian detection, animal detection, bicyclist detection) to match the local characteristics of each environment category, thereby optimizing processing resource allocation.
2Reliability
If all detection algorithms are activated simultaneously, then comprehensive subject detection is achieved, but false detections increase due to processing limitations
Solution Approach 1:
The system extracts and removes irrelevant detection algorithms from active operation based on the current environment category. In city environments, animal detection algorithms are extracted from the active set since animals are unlikely to be present. This reduction in active algorithms decreases processing load and eliminates a source of false detections while maintaining detection accuracy for relevant subjects.
3Reliability
If the system processes all environment types with full detection algorithms, then detection coverage is maximized, but processing resources are wasted in environments where certain detections are irrelevant
Solution Approach 1:
The system changes the operational parameters of detection algorithms based on the classified environment category. The activation state (on/off) of each detection algorithm is adjusted as a parameter according to the environment type. This parameter change optimizes processing resource utilization by activating only the necessary algorithms for each environment, reducing energy waste while maintaining appropriate detection coverage.
Data Source
AI summary
A safety system for a motor vehicle having a sensing arrangement (11) providing sensor signals related to the surrounding environment of the vehicle, at least one safety means (13, 14, 15) for an occupant of the vehicle, and a control means (22) adapted to control the safety means (13, 14, 15) depending on signals from the sensing arrangement (11). The safety system (10) has an environment classifying means (23) adapted to classify the surrounding environment of the vehicle into different predetermined categories on the basis of signals from the sensing arrangement (11), and to adjust the control means (22) depending on the vehicle environment category determined by the environment classifying means (23).