Autonomous Driving Mode Switching Feedback for Obstacle Safety
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Unmanned vehicles face safety concerns due to existing automatic driving systems' inability to handle obstacles, leading to potential collisions, which compromises their safety during travel.
Innovation Solution
A method for processing vehicle driving mode switching from unmanned to manned driving, where a target switching reason is determined, and status and environment information is sent to a server for analysis, enabling the automatic driving system to improve and avoid dangers upon encountering similar reasons in the future.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the unmanned vehicle uses an automatic driving system to reduce operating cost, then productivity is improved, but reliability deteriorates due to inability to handle obstacles and safety concerns
Solution Approach 1:
The system collects switching reason information when drivers take control from the autonomous system, transmits this feedback data to a server, and uses it to continuously optimize the autonomous driving algorithm. This feedback loop enables the system to learn from human intervention scenarios and improve its decision-making capabilities over time, thereby enhancing safety while maintaining autonomous operation benefits
Solution Approach 2:
The system performs preliminary classification and analysis of switching reasons before transmitting to the server. By pre-processing the data on the vehicle side (categorizing switching reasons into types such as obstacle avoidance, traffic rule compliance, etc.), the system prepares the feedback information in advance, enabling more efficient server-side analysis and faster algorithm optimization cycles
2Reliability
If the vehicle switches from unmanned to manned driving, then reliability is improved through human control, but productivity deteriorates due to loss of autonomous operation efficiency
Solution Approach 1:
Each manual takeover event generates structured feedback data about the specific scenario and switching reason, which is transmitted to the server for analysis. This continuous feedback mechanism allows the autonomous system to learn from human driver decisions and progressively improve its performance, reducing the frequency of manual takeovers over time and thereby preserving operational efficiency
Solution Approach 2:
The system replaces the need for continuous human monitoring and control with an intelligent autonomous driving system that uses sensor data, machine learning models, and real-time decision-making algorithms. This substitution maintains high operational efficiency while improving safety through consistent, error-free autonomous operation in suitable conditions
Data Source
AI summary
Embodiments of the present disclosure provide a method for processing vehicle driving mode switching, a vehicle and a server. The method includes: determining (S103) a target switching reason upon detecting that a driving mode of a vehicle is switched from unmanned driving to manned driving; acquiring (S104) status information and/or traveling environment information of the vehicle corresponding to the target switching reason; and sending the status information and/or the traveling environment information, and the target switching reason to a server, to enable the server to analyze the target switching reason, and improve the automatic driving system continuously according to the analysis result.