Machine Learning Model for Autonomous Vehicle Hazard Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in identifying and responding to unsafe events, such as near collisions, while navigating, as existing systems lack effective methods to accurately detect and mitigate potential hazards in real-time.
Innovation Solution
The development of machine learned models trained using sensor data and ground truth information to identify unsafe events, allowing autonomous vehicles to analyze input data and take appropriate actions to avoid collisions by classifying events as safe or unsafe and adjusting their trajectory or speed accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learned models are trained using sensor data and ground truth information to identify unsafe events, then measurement precision of unsafe event detection is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary training of machine learned models using sensor data and ground truth information before actual unsafe event detection. This pre-training phase prepares the models in advance, allowing them to accurately classify unsafe events during real-time operation without requiring complex runtime processing.
Solution Approach 2:
The patent introduces machine learned models as intermediary components between raw sensor data and unsafe event detection decisions. These models act as mediators that process and interpret sensor data, transforming complex raw inputs into reliable safety assessments, thereby improving detection precision while managing system complexity through specialized intermediate processing layers.
2Reliability
If autonomous vehicles take actions to avoid collisions by classifying events as safe or unsafe, then reliability of collision avoidance is improved, but loss of time increases due to real-time data processing
Solution Approach 1:
The system performs preliminary classification of events as safe or unsafe using trained machine learned models before collision avoidance actions are executed. This pre-classification enables rapid response by having safety decisions prepared in advance, reducing the time loss associated with real-time analysis during critical moments.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based collision avoidance systems with machine learned models that process and classify safety events. This substitution enables more reliable and faster decision-making by using intelligent algorithms that can rapidly assess complex sensor data patterns, improving collision avoidance reliability while minimizing response time delays.
Data Source
AI summary
Techniques and methods for training and/or using a machine learned model that identifies unsafe events. For instance, computing device(s) may receive input data, such as vehicle data generated by one or more vehicles and/or simulation data representing a simulated environment. The computing device(s) may then analyze features represented by the input data using one or more criteria in order to identify potential unsafe events represented by the input data. Additionally, the computing device(s) may receive ground truth data classifying the identified events as unsafe events or safe events. The computing device(s) may then train the machine learned model using at least the input data representing the unsafe events and the classifications. Next, when the computing device(s) and/or vehicles receive input data, the computing device(s) and/or vehicles may use the machine learned model to determine if the input data represents unsafe events.