Autonomous Driving Pattern Recognition for Liability Determination
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Solution Overview
Problem
Current autonomous driving technologies face challenges in determining responsibility in accidents involving autonomous vehicles, as they lack the ability to account for a driver's specific driving patterns and behaviors, making it difficult to assess liability.
Innovation Solution
A method and system that utilize training data from driver tutorials to recognize and store driving patterns, which are then used to control autonomous driving vehicles, providing evidence in case of accidents and enhancing accident avoidance by predicting collisions and adjusting speed based on surrounding situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous driving is controlled according to basic or preset policy without feedback from user, then automation level is improved, but ability to handle accidental situations and determine responsibility deteriorates
Solution Approach 1:
The system collects driving pattern data from the driver during tutorial phases and operational phases, storing this feedback information in a database. This feedback loop enables the system to learn and adapt to individual driver behaviors, improving its ability to handle accidental situations and determine responsibility while maintaining high automation levels.
Solution Approach 2:
The system performs preliminary data collection and pattern recognition during a tutorial phase before full autonomous operation begins. This preliminary action prepares the system with driver-specific information in advance, enabling better accident handling and responsibility determination when actual autonomous driving occurs.
2Reliability
If driver-specific driving patterns are collected and stored through tutorial training, then ability to handle accidental situations and determine responsibility is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system automatically collects driving pattern data during normal operation and tutorial phases without requiring external intervention. The autonomous driving controller continuously monitors and stores driving information, enabling the system to self-improve its accident handling capabilities without adding complex external data collection infrastructure.
3Reliability
If collision prediction and speed adjustment are performed based on surrounding situations, then accident avoidance capability is improved, but real-time processing requirements and computational load increase
Solution Approach 1:
The system pre-calculates collision prediction information based on stored driving patterns and surrounding situation data before accidents occur. By preparing prediction models and speed adjustment strategies in advance during the tutorial and data collection phases, the system reduces real-time computational requirements when actual accident avoidance decisions are needed.
Data Source
AI summary
A system of controlling an autonomous driving vehicle may include one or more processors; and memory storing executable instructions that, if executed by the one or more processors, configure the one or more processors to: execute a tutorial for recognizing a driving pattern of a driver, obtaining training data when the tutorial performs training of the driver, storing information associated with the driving pattern of the driver based on the obtained training data, and controlling autonomous driving of the autonomous driving vehicle based on the information associated with the driving pattern of the driver.