Off-Road Entry Waypoint Mapping for Autonomous Vehicle Behavior Prediction
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Solution Overview
Problem
Existing approaches for mapping off-road entry points in autonomous vehicles rely on heuristics, which may not be accurate in all instances, especially when generalizing to different geographic areas, leading to potential inaccuracies in predicting the behavior of other vehicles entering or exiting roadways.
Innovation Solution
A method and system that utilize machine-learned models trained on observations of actual road users to identify off-road entry lane waypoints by filtering lane waypoints based on alignment with the nearest lane's heading and scoring them based on trajectory frequencies, distance, and angle differences, allowing for more accurate behavior predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If heuristics-based approaches are used to identify off-road entry waypoints, then the implementation is simple and straightforward, but the accuracy of behavior predictions deteriorates
Solution Approach 1:
The patent replaces the mechanical/heuristic-based approach (rule-based algorithms) with a machine learning model that learns patterns from observed road user trajectories. The neural network model processes lane waypoint features and predicts off-road entry behavior, substituting the manual heuristic system with an intelligent system that adapts to real-world driving patterns.
Solution Approach 2:
The machine learning model is trained on actual road user observation data, allowing the system to self-improve and adapt to different geographic areas automatically. The model learns from historical trajectory data without requiring manual programming of heuristics for each specific location, enabling the system to serve itself by continuously improving its predictions based on accumulated data.
2Device complexity
If heuristics-based approaches are used to map off-road entry points, then the system complexity remains low, but the reliability of predictions for different geographic areas deteriorates
Solution Approach 1:
The machine learning model provides a universal solution that can be applied across different geographic areas and road configurations. Instead of requiring separate heuristic rules for each location, the single trained model generalizes to predict off-road entry behavior in diverse environments, making the system universally applicable while maintaining reliability.
Solution Approach 2:
The patent changes the fundamental parameters of the prediction system by transitioning from fixed heuristic rules to dynamic machine learning parameters that are optimized through training data. The model's internal parameters (weights and biases) are adjusted based on observed trajectories, enabling reliable predictions across varying geographic conditions without increasing apparent system complexity.
3Measurement precision
If machine-learned models are used to identify off-road entry lane waypoints, then the accuracy of behavior predictions is improved, but the device complexity increases
Solution Approach 1:
The machine learning model is trained in advance using historical road user observation data before deployment. This preliminary training action prepares the model with learned patterns and knowledge, so that during actual operation, the model can make accurate predictions without requiring complex real-time processing or additional infrastructure.
4Adaptability or versatility
If machine-learned models trained on actual road user observations are used, then the adaptability to different geographic areas is improved, but the loss of time for data collection and processing increases
Solution Approach 1:
The system performs data collection and model training in advance, before the autonomous vehicle needs to operate in a specific geographic area. By completing the adaptive learning process beforehand, the system eliminates the need for real-time data collection and processing during vehicle operation, thus reducing time loss while maintaining high adaptability.
Data Source
AI summary
Aspects of the disclosure provide a method of identifying off-road entry lane waypoints. For instance, a polygon representative of a driveway or parking area may be identified from map information. A nearest lane may be identified based on the polygon. A plurality of lane waypoints may be identified. Each of the lane waypoints may correspond to a location within at least one lane. The polygon and the plurality of lane waypoints may be input into a model. A lane waypoint of the plurality of lane waypoints may be selected as an off-road entry lane waypoint. The off-road entry lane waypoint may be associated with the nearest lane. The association may be provided to an autonomous vehicle in order to allow the autonomous vehicle to use the association to control the autonomous vehicle in an autonomous driving mode.