Autonomous Vehicle Trajectory Predictability Assessment for Safer Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in generating predictable trajectories, which are essential for safe and comfortable navigation, as they often need to align with human driver expectations to avoid confusion and ensure safety.
Innovation Solution
A system that assesses the predictability of candidate trajectories using a trajectory evaluation model, which processes predicted trajectories and probability scores to generate a predictability score, allowing the autonomous vehicle to select and follow the most predictable path, thereby reducing computational expense and noise in training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses machine-learning models to generate multiple candidate trajectories, then the vehicle can provide safer and more comfortable navigation, but the computational expense and processing time increase
Solution Approach 1:
The system pre-processes and encodes predicted trajectories into a reusable representation that can be efficiently evaluated against multiple candidate trajectories. This preliminary encoding action reduces the computational burden during real-time trajectory selection, allowing the system to evaluate multiple candidates quickly while maintaining safety.
Solution Approach 2:
The system creates an encoded copy of the predicted trajectories that captures essential information in a compact form. This encoded representation can be reused across multiple candidate trajectory evaluations without re-processing the original predicted trajectories, significantly reducing computational expense while maintaining evaluation accuracy.
2Adaptability or versatility
If the autonomous vehicle generates multiple predicted trajectories to assess predictability, then the vehicle can better align with human driver expectations, but the computational complexity and noise in training data increase
Solution Approach 1:
The system extracts only the essential features from multiple predicted trajectories to create a condensed encoded representation. This extraction process removes redundant information and noise while retaining the key characteristics needed for predictability assessment, reducing computational complexity while maintaining adaptability to human driver expectations.
Solution Approach 2:
The system transforms the trajectory data from its original high-dimensional form into an encoded representation with optimized parameters. This parameter transformation reduces the complexity of processing multiple predicted trajectories while preserving the information necessary to assess predictability and align with human driving behavior.
3Measurement precision
If the system evaluates multiple candidate trajectories against predicted trajectories, then the predictability assessment becomes more accurate, but the computational expense increases
Solution Approach 1:
The system performs preliminary encoding of predicted trajectories before they are used for evaluating multiple candidate trajectories. This advance preparation creates a reusable encoded representation that can be efficiently compared against many candidates, maintaining high assessment accuracy while reducing the energy cost of repeated evaluations.
Solution Approach 2:
The system creates an encoded copy of the predicted trajectories that preserves the essential information needed for accurate predictability assessment. This encoded copy can be reused across multiple candidate evaluations without re-processing the original data, significantly reducing computational energy consumption while maintaining measurement precision.
Data Source
AI summary
Data representing a set of predicted trajectories and a planned trajectory for an autonomous vehicle is obtained. A predictability score for the planned trajectory can be determined based on a comparison of the planned trajectory to the set of predicted trajectories for the autonomous vehicle. The predictability score indicates a level of predictability of the planned trajectory. A determination can be made, based at least on the predictability score, whether to initiate travel with the autonomous vehicle along the planned trajectory. In response to determining to initiate travel with the autonomous vehicle along the planned trajectory, a control system can be directed to maneuver the autonomous vehicle along the planned trajectory.