Adaptive Braking Time Threshold Model for Autonomous Vehicles
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Solution Overview
Problem
Existing autonomous braking systems face challenges in accurately determining the optimal time for braking or decelerating a vehicle to prevent collisions while minimizing interference with normal driving behavior, as premature braking can prevent accidents but late braking may not.
Innovation Solution
A time threshold model creation method and system that utilize braking data, including braking time, vehicle speed, and relative speed, to create a three-dimensional space, divide it into statistical regions, calculate probability distributions, and determine time thresholds using a percentage partitioning algorithm, resulting in a target curve surface for determining safe braking times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle is braked too early, then collision prevention is improved, but driver behavior interference increases
Solution Approach 1:
The patent applies dynamics by making the braking time threshold adaptive rather than fixed. The system dynamically adjusts the braking threshold based on real-time driving conditions, vehicle speed, and obstacle distance, allowing the braking decision boundary to move and change according to actual scenarios. This resolves the contradiction by enabling early braking when necessary (improving collision prevention) while avoiding unnecessary early braking during normal driving (reducing driver interference).
Solution Approach 2:
The patent changes the parameter of braking time threshold from a static value to a dynamic variable that depends on multiple factors including vehicle speed, obstacle distance, and driving conditions. By changing this critical parameter adaptively, the system achieves both collision prevention reliability and minimal driver interference, as the braking decision is made based on optimized parameter combinations rather than a fixed time threshold.
2Object-affected harmful factors
If the vehicle is braked too late, then driver behavior interference is reduced, but collision prevention capability deteriorates
Solution Approach 1:
The system uses dynamic threshold adjustment to ensure braking occurs at the optimal moment rather than too late. By continuously updating the braking time threshold based on real-time data and probability distributions, the system maintains high collision prevention capability while avoiding premature braking that would interfere with normal driving.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring driving conditions, obstacle proximity, and vehicle state, then using this feedback to adjust the braking threshold in real-time. This closed-loop approach ensures the system responds appropriately to changing conditions, preventing both late braking (which would reduce collision prevention) and early braking (which would increase driver interference).
3Device complexity
If a fixed braking threshold is used, then system complexity is reduced, but braking accuracy deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the continuous range of driving conditions into discrete statistical regions based on vehicle speed and obstacle distance. Each region has its own optimized braking threshold derived from probability distribution analysis of historical data. This segmentation approach improves braking accuracy for different scenarios while keeping the system manageable through structured data organization and region-based decision rules.
Solution Approach 2:
The patent transitions from a one-dimensional fixed time threshold to a multi-dimensional threshold surface that considers vehicle speed, obstacle distance, and their interactions. By adding these dimensions and creating a threshold surface in the speed-distance-time space, the system achieves high braking accuracy across diverse scenarios while maintaining reasonable complexity through the use of probability distribution models and region-based approaches.
Data Source
AI summary
A time threshold model creation method and a time threshold model creation system based on an autonomous braking system are provided. The method includes the steps of: obtaining braking data about a target vehicle; creating a three-dimensional space in accordance with a braking time, a speed of the target vehicle, and a relative speed, and obtaining a point cloud of the braking data in the three-dimensional space; dividing a two-dimensional plane defined by the speed of the target vehicle and the relative speed into a plurality of statistical regions, and calculating a probability in each statistical region to obtain a fitted probability distribution curve; calculating a time threshold in each statistical region in accordance with the fitted probability distribution curve; and obtaining a target curve surface of the time thresholds in accordance with the distribution of the time threshold in each statistical region in the three-dimensional space.