Vehicle Braking Distance Evaluation Using Road Type Detection
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Solution Overview
Problem
Current vehicle braking distance calculations often set a higher value than necessary, incorporating a significant safety margin, which can cause driver irritation and inefficiency in semi- or fully autonomous vehicles.
Innovation Solution
A method using a trained neural network to evaluate the minimum braking distance by determining road type and vehicle characteristics through image and sensor data, adjusting the braking distance based on real-time conditions, and historical information to reduce unnecessary safety margins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a higher braking distance with significant safety margin is set, then safety and reliability are improved, but driver irritation increases and system efficiency deteriorates
Solution Approach 1:
The braking distance is made dynamic rather than static. The system continuously adapts the braking distance based on real-time road type detection through neural networks and database comparison, allowing the safety margin to vary according to actual conditions rather than maintaining a fixed conservative value at all times
Solution Approach 2:
The system changes the parameter of braking distance based on detected road conditions. By identifying different road types (ice, snow, wet, dry) through image analysis and comparing with database information, the system adjusts the braking distance parameter to match actual road characteristics, reducing unnecessary margins on suitable surfaces while maintaining safety on hazardous surfaces
2Reliability
If a higher braking distance with significant safety margin is set, then safety and reliability are improved, but productivity and traffic flow efficiency deteriorate
Solution Approach 1:
The braking distance adapts dynamically to road conditions, enabling tighter following distances on dry roads to improve traffic flow efficiency while automatically increasing distance on hazardous surfaces to maintain safety
Solution Approach 2:
The system changes braking distance parameters based on road type detection, allowing for optimized spacing between vehicles on different surface conditions, thereby improving overall traffic productivity without compromising safety
3Reliability
If a fixed conservative braking distance is used, then safety is improved, but measurement precision and accuracy of braking distance calculation deteriorate
Solution Approach 1:
The system segments the road surface into different types (ice, snow, wet, dry) and applies appropriate braking distance calculations for each segment type, rather than using a single conservative value for all conditions
Solution Approach 2:
The system replaces conservative mechanical estimation with neural network-based image analysis and database comparison to accurately identify road types, enabling precise braking distance calculations tailored to actual surface conditions
Data Source
AI summary
A method for evaluating a minimum braking distance of a vehicle, in particular a car. The method comprises the step of obtaining at least one image in a movement direction of the vehicle associated substantially with an actual location of vehicle. A first road type indication from the at least one image is determined by a trained neural network architecture. Second road type indication associated with the actual location of the car are obtained from a database and compared with the first road type indication. If the second road type indication supports the determined first road type indication, an adjustment parameter associated with one of the at least first and second road type indication is selected. If second road type indication does not support the determined first road type indication, a default adjustment parameter as adjustment parameter is selected. Finally, a minimum braking distance using the adjustment parameter is set.