Environment Encoding for Autonomous Vehicle State Prediction
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Solution Overview
Problem
Autonomous vehicles face challenges in making timely and reasonable decisions regarding their trajectory due to limitations in computational power and the unpredictability of other entities' behaviors in real-world traffic environments, despite advancements in hardware and software.
Innovation Solution
The development of methods and apparatus for generating joint state predictions using machine learning models, such as deep neural networks, which encode the vehicle's environment and predict the actions of moving entities, allowing for continuous decision-making and trajectory planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If computational power is increased to enable timely navigation decisions, then decision-making speed is improved, but hardware complexity and cost increase
Solution Approach 1:
The patent replaces traditional rule-based decision-making systems with machine learning models that learn optimal navigation strategies from data. This substitution enables faster decision-making by leveraging trained model inference rather than complex real-time computations, reducing the computational burden on hardware while improving decision speed.
Solution Approach 2:
The system performs preliminary training of machine learning models offline using historical data, so that during actual operation, the vehicle can make rapid decisions by applying pre-trained knowledge. This preliminary action transfers computational work from real-time operation to offline training, enabling fast decision-making with moderate hardware requirements.
2Measurement precision
If more sensors and data collection are used to improve environment understanding, then measurement precision is improved, but device complexity and energy consumption increase
Solution Approach 1:
The machine learning models are designed to process multiple types of sensor data (camera images, LIDAR points, radar signals) through unified architectures that can handle diverse input formats. This multi-functionality allows the system to achieve high measurement precision using various sensors without proportionally increasing system complexity, as the same processing framework handles different data types.
Solution Approach 2:
The system extracts only the most relevant features from raw sensor data using learned representations, rather than processing all raw data. This extraction of essential information maintains measurement precision while reducing the computational burden and system complexity required to handle complete sensor datasets.
3Reliability
If real-time processing of all sensor data is performed, then decision accuracy is improved, but processing time increases
Solution Approach 1:
The patent replaces exhaustive real-time data processing with inference from pre-trained machine learning models. These models have already learned patterns and relationships from extensive training data, allowing them to make accurate decisions rapidly during operation without requiring complete real-time processing of all sensor inputs, thus maintaining accuracy while reducing processing time.
Solution Approach 2:
The system processes a subset of the most critical features and data points rather than all available sensor data in real-time. By focusing on partial processing of key information that has been identified as most important during training, the system achieves sufficient decision accuracy with significantly reduced processing time compared to comprehensive real-time analysis.
Data Source
AI summary
An encoding of an environment for operating vehicles is obtained, comprising a combination of at least a representation of moving entities with a graph representation of static infrastructure elements. Using the encoding and a set of one or more observations of the environment state, a machine learning model is trained to produce a probabilistic representation of a set of predicted states of the environment. A trained version of the machine learning model is stored and deployed at one or more vehicles to help plan and control vehicle movements.