Obstacle Course Prediction with Interference Assessment
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Solution Overview
Problem
Conventional obstacle prediction techniques are inadequate for complex traffic environments with multiple obstacles, as they only predict the course of a single obstacle, failing to account for interactions between multiple vehicles.
Innovation Solution
A method and apparatus that predict the course of obstacles by considering their positions and internal states, performing probabilistic predictions for multiple obstacles, assessing course interference, and calculating the probability of each course, including trajectory generation in space-time to account for dynamic changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional single-obstacle prediction technique is used, then the prediction process is simple, but it cannot handle complicated traffic environments with multiple obstacles
Solution Approach 1:
The prediction process is segmented into distinct functional modules: a prediction unit that performs probabilistic prediction of multiple courses for each obstacle, and a course interference assessment unit that evaluates interactions between obstacles. This segmentation allows the system to handle multiple obstacles systematically while maintaining manageable complexity through modular design.
Solution Approach 2:
The system transitions from predicting a single deterministic course to predicting multiple probabilistic courses simultaneously. By adding the dimension of probability and considering multiple possible trajectories, the system can accommodate the uncertainty and complexity of multi-obstacle environments while providing comprehensive safety assessment.
2Measurement precision
If probabilistic prediction of multiple courses is performed for multiple obstacles, then prediction accuracy in complex environments improves, but calculation complexity increases
Solution Approach 1:
The system performs probabilistic prediction of multiple possible courses for each obstacle in advance, before assessing their interactions. This preliminary action generates a set of candidate trajectories with associated probabilities, which are then evaluated for interference. This approach enables accurate prediction while managing computational complexity through staged processing.
Solution Approach 2:
The course interference assessment unit acts as an intermediary that evaluates the interactions between predicted courses of multiple obstacles. It takes the probabilistic courses generated by the prediction unit and assesses their interference, lowering probabilities for courses that would result in collisions. This intermediary layer enables accurate multi-obstacle prediction while maintaining systematic computational management.
3Reliability
If course interference assessment is performed to lower probability of interfering courses, then collision risk reduction improves, but processing time increases
Solution Approach 1:
The course interference assessment unit provides feedback by lowering the predicted probability of courses that would result in interference between obstacles. This feedback mechanism systematically reduces collision risk by adjusting probabilities based on interference assessment, enabling reliable collision avoidance while maintaining efficient processing through iterative probability adjustment.
Data Source
AI summary
A method, an apparatus, and a program of predicting an obstacle course, capable of appropriately predicting a course of an obstacle even under a complicated traffic environment, are provided. The course, which the obstacle may take, is predicted based on the position and the internal state of the obstacle, and at the time of the prediction, a plurality of courses are probabilistically predicted for at least one obstacle. When there are a plurality of obstacles, the course in which different obstacles interfere with each other is obtained from the predicted courses, which a plurality of obstacles may take, and the predictive probability of the course for which the probabilistic prediction is performed from the courses in which they interfere with each other is lowered. Probability of realizing each of a plurality of courses including the course of which predicted probability is lowered is calculated.