Autonomous Vehicle Latency Violation Prevention via Runtime Heatmaps
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Solution Overview
Problem
Autonomous vehicle control systems face latency violations due to the processing of large volumes of data in busy environments, leading to increased reaction times and potential safety risks, especially under adverse conditions like heavy rain or snow.
Innovation Solution
The implementation of a method that predicts latency violations by determining runtime performance in various regions, generating heatmaps based on environmental conditions, and adjusting operating parameters to maintain reaction time, utilizing machine learning models for training neural networks and fleet management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle processes large volumes of data in busy environments, then the navigation and routing tasks can be performed more accurately, but the reaction time increases and latency violations occur
Solution Approach 1:
The system performs preliminary actions by predicting latency violations before they occur. The latency predictor module analyzes runtime performance data and environmental conditions to forecast potential latency issues, allowing the vehicle to proactively adjust processing strategies or resource allocation before actual latency violations impact navigation accuracy or reaction time
Solution Approach 2:
The system dynamically adjusts operating parameters based on real-time conditions. The control system modifies processing priorities, resource allocation, and operational modes according to current environmental complexity and predicted latency risks, enabling flexible optimization between navigation accuracy and reaction time requirements
2Reliability
If the control system processes more environmental data, then the safety in adverse conditions improves, but the latency increases
Solution Approach 1:
The system performs preliminary risk assessment by analyzing runtime performance data and environmental conditions to predict latency violations before they occur. This allows the vehicle to proactively adjust processing strategies for safety-critical functions in adverse conditions, maintaining reliability while preventing latency-related safety compromises
Solution Approach 2:
The system implements continuous feedback loops where the latency predictor monitors processing performance and feeds this information back to the control system. This feedback enables real-time adjustments to data processing priorities and resource allocation, ensuring safety requirements are met while minimizing latency in adverse environmental conditions
Data Source
AI summary
Disclosed are systems, apparatuses, methods, and computer-readable media to autonomous driving vehicles and, in particular, for preventing latency violations in autonomous vehicle control systems. A method includes navigating the autonomous vehicle into first region of an environment at a first time, determining a runtime performance of the autonomous vehicle control system in the first region based on the environment, recording the runtime performance into runtime information of the autonomous vehicle, and determining a route to a destination location for the autonomous vehicle to navigate based on mapping information determined based on the runtime information.