Autonomous Vehicle Path Prediction With Semantic Behavior Filtering
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Solution Overview
Problem
Existing autonomous vehicle trajectory determination methods require high computational power, leading to inefficient computation and slowed reaction times, which can result in real-world complications.
Innovation Solution
Implementing semantic behavior filtering to predict actions of agents in a scene by filtering out low-quality predictions based on contextual information, reducing compute resources and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional path determination methods are used, then comprehensive trajectory options can be generated, but computational power requirements become excessively high
Solution Approach 1:
The patent segments the path determination process into distinct modules: prediction module (generating candidate trajectories), evaluation module (assessing trajectories against constraints), and selection module (choosing optimal path). This segmentation allows each module to focus on specific computational tasks, reducing overall computational power requirements while maintaining comprehensive trajectory analysis
Solution Approach 2:
The prediction module generates candidate trajectories in advance before the evaluation phase. By pre-computing multiple possible paths based on historical data and current state, the system reduces real-time computational burden during critical decision-making moments, allowing the evaluation module to work with pre-prepared options rather than generating them on-the-fly
2Reliability
If comprehensive predictions are made for all agents, then all possible behaviors are considered, but reaction time becomes too slow
Solution Approach 1:
The patent extracts and prioritizes only the most relevant agents for prediction based on their proximity to the autonomous vehicle and potential impact on trajectory. By taking out and focusing computational resources on critical agents rather than all agents in the environment, the system maintains comprehensive prediction for important targets while reducing overall computation time to meet reaction time requirements
Solution Approach 2:
Different prediction thoroughness is applied to different agents based on their local importance. High-priority agents (close proximity, potential conflict) receive detailed multi-trajectory predictions, while low-priority agents receive simplified or aggregated predictions. This local quality differentiation maintains prediction completeness for critical decisions while reducing time loss from unnecessary comprehensive analysis of all agents
3Measurement precision
If all predictions are processed, then no high-quality predictions are lost, but computational efficiency decreases
Solution Approach 1:
The system dynamically adjusts the level of prediction processing based on contextual factors such as scene complexity, vehicle speed, and proximity to intersections or pedestrians. In high-risk scenarios, the system processes more predictions with higher quality thresholds; in low-risk scenarios, it reduces processing depth. This dynamic adaptation maintains prediction quality when needed while improving computation efficiency during normal operations
Data Source
AI summary
Provided are methods and systems for semantic behavior filtering for prediction improvement. A method for operating an autonomous vehicle is provided. The method includes obtaining, by at least one processor, semantic image data associated with an environment in which an autonomous vehicle is operating. The method includes determining, by the at least one processor, at least one agent in the environment. The method includes determining a predicted action for the at least one agent. The method includes determining an agent predicted path for the at least one agent. The method includes determining a vehicle path of the autonomous vehicle. The method includes determining a predicted collision of the at least one agent and the autonomous vehicle. The method includes simulating actions to avoid the predicted collision. The method includes categorizing the predicted collision as a primary predicted collision based on the simulating actions.