Autonomous Driving Decision Tuning With Trajectory-Pair Cost Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous driving technologies face challenges in handling dynamic and complex environmental changes due to limited training scenarios, leading to inefficiencies in decision-making and insufficient interpretability, particularly in rule-based and learning-based methods.
Innovation Solution
A data-driven autonomous driving decision optimization system combining rule-based and learning-based approaches, utilizing large-scale human driving data to enhance decision-making performance, incorporating a data production, screening, model encapsulation, and parameter tuning modules to optimize decision parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If rule-based methods are used for decision-making, then interpretability is improved, but decision-making performance shows significant gap compared to skilled drivers
Solution Approach 1:
The patent combines rule-based methods and learning-based methods into a hybrid decision-making system. The learning-based component (neural network) processes input features and generates predictions, while the rule-based component (decision rules with thresholds) provides interpretability and safety constraints. This merging allows the system to achieve both high performance (through learning) and interpretability (through rules), resolving the contradiction between these two requirements.
2Reliability
If learning-based methods are used for decision-making, then decision-making performance is improved, but interpretability becomes insufficient
Solution Approach 1:
The patent introduces decision rules as an intermediary layer between the learning-based prediction module and the final decision output. The neural network generates predicted values, which are then processed through configurable decision rules (thresholds, conditions, and actions) that provide interpretability. This intermediary structure allows the system to maintain high performance from the learning model while adding transparent, explainable decision logic.
3Productivity
If training is limited to specific scenarios or tasks, then training efficiency is improved, but ability to handle dynamic and complex environmental changes deteriorates
Solution Approach 1:
The patent designs a universal decision-making system with configurable decision rules that can adapt to multiple scenarios and tasks. The system uses a unified neural network architecture with scenario-specific input features and configurable decision thresholds, allowing it to handle diverse driving situations (lane changes, merges, intersections, etc.) without requiring separate specialized models for each scenario, thus achieving both efficiency and adaptability.
4Reliability
If data-driven approach with large-scale data is used, then performance ceiling of decision system is enhanced, but system complexity increases
Solution Approach 1:
The patent segments the complex data-driven decision-making system into distinct modular components: feature extraction module, neural network prediction module, decision rule configuration module, and threshold optimization module. Each module handles a specific aspect of the decision-making process, making the overall system more manageable and easier to implement despite utilizing large-scale data for training.
Data Source
AI summary
A data-driven autonomous driving decision optimization system and method, comprising: a data production module, a data screening module, a model encapsulation module, and a parameter tuning module; the data production module takes human driving data as input, annotation, preprocessing, format conversion, extracts key features and performs standardization, normalization, and encoding to generate raw data for the data-driven process that meets the algorithm input requirements; the data screening module screens corresponding data from training data by the decision-making module and performs effective classification; the model encapsulation module encapsulates C++ decision code and constructs a trajectory-pair evaluation cost map required for training using a ground-truth evaluation method based on trajectory pairs; the parameter tuning module, based on screened data under different scenarios, the encapsulated decision algorithm model, and the trajectory-pair evaluation cost map, employs black-box optimization to obtain decision parameters for the corresponding scenarios under the current decision algorithm.