Autonomous Vehicle Junction Track Prediction Using Historical Data
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Solution Overview
Problem
Self-driving vehicles face increased driving risks at junctions due to incomplete or missing lane lines in maps, leading to inaccurate decision-making when predicting the movements of other vehicles.
Innovation Solution
A vehicle track prediction method and device that uses historical traveling data and current states to predict the potential track of obstacle vehicles, allowing self-driving vehicles to make decisions independently of lane lines, by determining selectable exits and using face recognition technology for accurate driver identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving decisions are made based on lane lines provided by a map, then the self-driving vehicle can follow predefined paths, but the decision-making accuracy deteriorates when lane lines are incomplete or missing at junctions
Solution Approach 1:
The patent introduces an intermediary prediction module that uses historical trajectory data and behavior patterns as a mediator between the map lane lines and the final driving decision. When lane lines are unavailable or unreliable, the system uses predicted tracks of obstacle vehicles based on their historical behavior at junctions to make driving decisions, thus resolving the contradiction between following predefined paths and achieving accurate predictions in complex scenarios
Solution Approach 2:
The system performs preliminary actions by collecting and analyzing historical trajectory data of obstacle vehicles before making driving decisions. The prediction module pre-processes trajectory information and identifies behavioral patterns at junctions in advance, so that when the vehicle approaches a junction with incomplete lane lines, the decision-making system already has pre-computed prediction results to rely on, improving both reliability and accuracy
2Adaptability or versatility
If the self-driving vehicle relies on map lane lines for navigation, then the system complexity remains low, but the adaptability deteriorates when encountering junctions with missing or inaccurate lane line data
Solution Approach 1:
The patent segments the navigation system into multiple independent modules: a map lane line processing module, a historical trajectory data collection module, a prediction module, and a decision-making module. This segmentation allows the system to adapt to different junction scenarios by activating appropriate modules while keeping overall system complexity manageable through modular design. The prediction module specifically handles junctions with incomplete lane lines without requiring complete redesign of the entire navigation system
Solution Approach 2:
The prediction module serves multiple functions: it predicts obstacle vehicle tracks, identifies potential collision risks, and provides alternative path suggestions. This multi-functionality enhances the system's adaptability to various junction scenarios without proportionally increasing complexity, as a single modular component handles multiple prediction and decision-making tasks
Data Source
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AI summary
A vehicle track prediction method and device, a storage medium and a terminal device are provided according to embodiments of the present invention. The method includes: determining an obstacle vehicle entering a junction region in a case that an autonomous vehicle enters the junction region; acquiring historical travelling data of the obstacle vehicle in the junction region; predicting a potential track of the obstacle vehicle according to the historical travelling data and a current travelling state of the obstacle vehicle; and predicting a track of the autonomous vehicle in the junction region according to the potential track of the obstacle vehicle and a current travelling state of the autonomous vehicle. A decision making accuracy in self-driving may be effectively improved, and a driving risk may be reduced.