Autonomous Vehicle Behavior Planning via Intent-Motion Fusion
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately and efficiently estimating future interactions of traffic participants for collision-free path planning, as existing methodologies like driver intent estimation and motion prediction only capture partial information and are difficult to combine effectively.
Innovation Solution
A behavior planning and decision-making system for autonomous vehicles that combines intent estimation and motion prediction models using a function approximator to integrate multiple sources of information, allowing for intelligent prediction of future traffic participant motions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If driver intent estimation or motion prediction models are applied independently to predict future motion of traffic participants, then the prediction process is simple, but the prediction accuracy is insufficient because each model only captures partial information
Solution Approach 1:
The patent combines multiple independent prediction models (driver intent estimation model and motion prediction model) into a unified framework. The ensemble model integrates outputs from both models to predict future motion of traffic participants, thereby capturing both behavioral intent and motion dynamics for improved prediction accuracy while managing integration complexity through structured combination strategies.
2Reliability
If multiple prediction models are combined to improve prediction accuracy, then the prediction becomes more comprehensive, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary processing by pre-computing features from track histories and pre-processing sensor data before feeding them to multiple prediction models. This preliminary action reduces the computational burden during real-time prediction, allowing the ensemble of multiple models to operate more efficiently and reduce overall processing time while maintaining comprehensive and reliable predictions.
3Productivity
If the same input data is processed by both intent estimation and motion prediction models, then the data processing is efficient, but the information captured remains partial and incomplete
Solution Approach 1:
The patent segments the input data into different feature sets tailored for each model type. The driver intent estimation model processes features related to behavioral intent (e.g., maneuver preferences, interaction context), while the motion prediction model processes features related to physical motion (e.g., velocity, acceleration, trajectory). This segmentation allows each model to specialize in its domain, capturing complementary information without redundant processing, thereby improving both information completeness and processing efficiency.
Data Source
AI summary
Methods and systems for decision making in an autonomous vehicle (AV) are described. A vehicle control system may include a control unit, a perception unit, and a behavioral planning unit. The behavioral planning unit may include an intent estimator that receives a first set of perception information from the perception unit. The behavioral planning unit may include a motion predictor that receives the first set of perception information from the perception unit. The behavioral planning unit may include a function approximator that receives a second set of perception information from the perception unit. The second set of perception information is smaller than the first set of perception information. The function approximator determines a prediction, and the control unit uses the prediction to control an operation of the AV.