Remote Driving Control Using Predicted Network Quality Shifts
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Solution Overview
Problem
Remote driving systems face safety hazards due to sudden changes in network quality, leading to potential delays or losses in information transmission between remotely driven vehicles and remote driving servers.
Innovation Solution
A remote driving control method that predicts network quality changes within a target time period, allowing for the adjustment of driving control policies in advance to mitigate the impact of network quality changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If remote driving control is implemented using network transmission between vehicle and server, then driving functionality is improved, but network instability causes information loss or delay leading to safety hazards
Solution Approach 1:
The system performs preliminary actions by predicting network quality changes before they occur. The prediction module forecasts network parameters (bandwidth, latency, packet loss) for future time points, and the control module pre-adjusts driving policies based on these predictions, allowing the system to prepare compensatory measures in advance rather than reacting after problems occur.
Solution Approach 2:
The system implements beforehand cushioning by establishing a buffer mechanism through policy adjustment. When network quality degradation is predicted, the control module modifies driving policies to be more conservative or robust, creating a cushion that protects against potential information loss or delay, thereby maintaining safety even when network conditions deteriorate.
2Reliability
If network quality prediction and policy adjustment are implemented, then travel safety is improved, but system complexity increases
Solution Approach 1:
The control system is segmented into distinct functional modules: a prediction module that forecasts network quality, and a control module that adjusts driving policies. This segmentation allows each module to specialize in its function, making the overall complex system more manageable and maintainable while enabling sophisticated safety mechanisms.
Solution Approach 2:
The prediction module acts as an intermediary between the network environment and the control module. Instead of the control module directly reacting to network conditions, the prediction module processes network quality forecasts and translates them into actionable insights for policy adjustment, simplifying the control logic while improving safety.
Data Source
AI summary
Provided are a remote driving control method and apparatus, a computer device, and a storage medium, belonging to the field of remote driving technologies. The method may include: predicting network quality between a remotely driven vehicle and a remote driving server within a target time period, the network quality prediction including a predicted network parameter corresponding to each time point within the target time period; determining, from the target time period according to the predicted network parameter corresponding to each time point within the target time period and a current network parameter between the remotely driven vehicle and the remote driving server, a target time point at which network quality changes; and adjusting a driving control policy of the remotely driven vehicle based on a predicted network parameter corresponding to the target time point, to control the remotely driven vehicle according to an adjusted driving control policy.