Probabilistic Collision Avoidance for Vehicle Path Prediction
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Solution Overview
Problem
Existing automatic emergency brake systems in vehicles often result in unnecessary or faulty braking due to inaccuracies in sensor data processing, leading to unreliable collision prevention in dynamic driving situations.
Innovation Solution
A method that predicts multiple vehicle paths and object paths, weights them with probability, and calculates an overall collision probability to trigger a collision avoidance maneuver only when the threshold is exceeded, using a combination of imaging sensors and decision-making algorithms to comprehensively assess the driving situation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional emergency brake systems use simple sensor data processing, then the system response is fast, but the collision detection accuracy deteriorates due to sensor inaccuracies
Solution Approach 1:
The patent segments the collision assessment into multiple independent vehicle paths, each evaluated separately with its own collision probability calculation. This allows the system to process multiple scenarios in parallel (maintaining speed) while reducing the impact of individual sensor errors through diversification (improving accuracy).
Solution Approach 2:
The system changes the parameter representation from single deterministic sensor readings to probabilistic distributions. By representing vehicle paths and object paths as probability-weighted sets, the system can account for sensor inaccuracies without sacrificing response speed, as the probabilistic framework naturally handles uncertainty.
2Reliability
If the system triggers collision avoidance maneuvers frequently to ensure safety, then collision prevention reliability improves, but false alarms increase due to misinterpretations
Solution Approach 1:
The system dynamically adjusts the collision assessment by continuously evaluating multiple vehicle paths and their probabilities. Rather than relying on a single static assessment, the dynamic evaluation of multiple scenarios allows the system to distinguish between genuine collision risks and sensor errors, reducing false alarms while maintaining reliability.
Solution Approach 2:
The probabilistic collision assessment provides feedback on the confidence level of detected collisions. By calculating overall collision probabilities from multiple path evaluations, the system can distinguish between high-confidence genuine risks (triggering avoidance maneuvers) and low-confidence false alarms (filtered out), thus reducing false alarm incidence while maintaining reliability.
3Measurement precision
If multiple vehicle paths and object paths are predicted and evaluated with probabilities, then collision detection accuracy improves, but the computational complexity increases
Solution Approach 1:
The system evaluates multiple vehicle paths and object paths, but focuses computational resources on the most probable paths and those with highest collision risk. By partially evaluating all possible paths rather than exhaustively processing every combination, the system achieves high detection accuracy while limiting computational complexity to manageable levels.
4Reliability
If comprehensive sensor data processing is performed to account for inaccuracies, then decision-making reliability improves, but the processing time increases
Solution Approach 1:
The comprehensive data processing is segmented into independent vehicle path evaluations that can be executed in parallel. Each path is processed separately with its own probability calculation, allowing the system to account for sensor inaccuracies comprehensively while maintaining efficient processing time through parallel computation architecture.
Data Source
AI summary
In a method for automated avoidance of a collision of a vehicle with an object in the surroundings of the vehicle, multiple vehicle paths are predicted and each one is weighted with a vehicle path probability, the vehicle surroundings are recorded with an imaging vehicle sensor, an object in the vehicle surroundings is captured, at least one object path in the vehicle surroundings is predicted and is weighted with an object path probability, one of the vehicle paths is tested for collision with the at least one object path and if a collision is possible, a collision probability with the at least one object path is calculated, a weighting criterion for an overall collision probability of the vehicle with the object is ascertained and a test is performed of whether the weighting criterion exceeds a threshold and if the threshold is exceeded a collision avoidance maneuver is triggered.