Multi-Track Collision Risk Calculation for Complex Road Scenarios
Solution Overview
Problem
Existing collision possibility acquiring apparatuses struggle to accurately calculate the collision risk at crossroads and similar complex road scenarios, as they typically only consider one predicted track of obstacles, leading to reduced accuracy.
Innovation Solution
The apparatus and method acquire multiple predicted tracks of both the own vehicle and obstacles, using sensors and predictive algorithms to calculate collision possibilities based on various possible paths, enhancing accuracy by considering multiple scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only one predicted track of the obstacle is calculated, then the calculation process is simple and fast, but the collision possibility calculation accuracy deteriorates when the road has many branches
Solution Approach 1:
The patent segments the obstacle's possible movement paths into multiple discrete tracks (first track, second track, third track) based on road branch points. Instead of calculating a single continuous predicted track, the system divides the possible trajectories into separate segments, each corresponding to a different road choice. This segmentation enables accurate collision assessment at branch points while maintaining computational efficiency by processing discrete path options rather than continuous possibilities.
Solution Approach 2:
The patent dynamically adjusts the number of tracks calculated based on the road environment. When the obstacle is detected to be approaching a branch point, the system calculates multiple possible tracks (first, second, and third tracks corresponding to different road directions). When not at branch points, the system uses a single predicted track. This dynamic adaptation allows the system to maintain high accuracy when needed while avoiding unnecessary computational complexity in straightforward scenarios.
2Reliability
If multiple tracks of the obstacle are acquired, then the collision possibility calculation accuracy improves in complex road environments, but the computational time and processing load increase
Solution Approach 1:
The patent performs preliminary identification of branch points and pre-calculation of possible tracks before actual collision risk assessment. The system detects when the obstacle is approaching a branch point and pre-determines the set of possible tracks (first, second, third tracks) that will be needed for collision assessment. This preliminary action ensures that when collision calculation is required, the track data is already prepared, reducing real-time computational burden while maintaining high reliability in complex environments.
Solution Approach 2:
The patent calculates multiple tracks (excessive action) only when necessary, specifically when the obstacle is detected to be approaching a branch point. In straightforward road scenarios without branches, the system uses a single predicted track (partial action). This selective application of multiple-track analysis ensures high reliability when needed while minimizing unnecessary computational time in simple scenarios.
3Adaptability or versatility
If the own vehicle or obstacle is at a crossroad with many branches, then the situation requires complex multi-track analysis, but conventional single-track methods fail to accurately calculate collision possibility
Solution Approach 1:
The patent creates a universal collision assessment framework that works across diverse road environments. The system uses the same basic collision calculation methodology whether the scenario involves a single straight road or a complex multi-branch crossroad. By establishing a universal approach that accommodates both simple and complex scenarios through the same apparatus and method, the system achieves high adaptability while maintaining measurement precision through consistent multi-track analysis when branches are detected.
Data Source
AI summary
An own vehicle risk acquiring ECU 1 acquires a predicted track of an own vehicle and calculates and acquires a plurality of tracks of the other vehicle about the own vehicle. According to the predicted track of the own vehicle and the plurality of tracks of the other vehicle, a collision probability of the own vehicle is calculated as a collision possibility.