Autonomous Vehicle System Load Warning for Safety Operator Alerts
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Solution Overview
Problem
Autonomous driving vehicles face performance degradation when under heavy system load, particularly in complex driving environments, which can lead to reduced ability to handle safety-critical situations, placing a demanding burden on human operators to constantly monitor and take control.
Innovation Solution
A system that monitors system load parameters such as CPU usage and end-to-end latency, generating warning messages when thresholds are exceeded, allowing safety operators to take over control, thereby alleviating the need for constant attention and ensuring safe operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous driving system performs intensive computations to handle complex driving environments, then the system's ability to handle complicated scenarios is improved, but the system load increases causing performance degradation
Solution Approach 1:
The system performs preliminary actions by predicting future system load conditions based on historical data and current trends. The warning message is generated in advance when load thresholds are projected to be exceeded, allowing the safety operator to prepare for potential takeover before performance degradation actually occurs. This proactive approach resolves the contradiction by anticipating and preventing reliability issues before they manifest.
2Reliability
If a human operator constantly monitors the autonomous vehicle to take over control when needed, then safety is improved, but operator workload increases
Solution Approach 1:
The system implements feedback by continuously monitoring system load parameters and providing real-time warning messages to the safety operator when thresholds are exceeded. This automated feedback mechanism replaces the need for constant operator monitoring, as the system itself detects and communicates critical conditions. The operator only needs to respond when the system actively signals a problem, dramatically reducing workload while maintaining safety.
3Extent of automation
If the autonomous driving system operates without human interaction, then automation level is improved, but safety response capability deteriorates when system load is heavy
Solution Approach 1:
The system dynamically adjusts its operational mode based on real-time system load conditions. When load is within acceptable thresholds, the system operates in full autonomous mode without human interaction. When load exceeds thresholds and performance degradation is detected, the system dynamically transitions to a state that activates safety operator alerts and enables manual takeover capability. This dynamic adaptation resolves the contradiction by maintaining high automation during normal operation while ensuring safety capability is restored when needed.
Data Source
AI summary
According to one embodiment, a method of generating warning messages based on system load of an autonomous driving vehicle can relieve a safety operator of the burden of constantly monitoring the vehicle and outside driving environments. The method uses a threshold for each of a number of system load parameters to determine whether the vehicle has a heavy system load that needs the attention of the safety operator. In one example, the vehicle can use a CPU usage threshold and an end-to-end latency threshold to determine whether the vehicle has a heavy system load while travelling on a road segment. If any of the thresholds is exceeded, the vehicle can send a warning message to the safety driver. The system load thresholds may be determined from data collected from the autonomous driving vehicle when it previously travelled on the road segment.