Customized Driver Transition Plan for Autonomous Vehicles
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Solution Overview
Problem
The transition period between autonomous and manual driving modes in self-driving vehicles is critical and risky, as it involves the potential for human error and system malfunction, with existing technologies lacking effective mechanisms to ensure safe and optimized transitions based on contextual factors and driver performance.
Innovation Solution
A cognitive system generates a customized transition plan for each driver and vehicle type, considering contextual factors like weather, traffic, and road conditions, using IoT devices and machine learning to monitor and adjust the transition process, ensuring compliance with driver performance requirements and optimizing the transition time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a standardized transition protocol is used for all drivers, then the system complexity is reduced and ease of operation is improved, but the reliability and safety of the transition process deteriorates due to inability to account for individual driver performance and contextual factors
Solution Approach 1:
The system performs preliminary assessment of driver performance metrics and contextual factors before the transition begins. Driver capability is evaluated in advance, and the transition plan is pre-customized based on this assessment, ensuring that safety requirements are met before the actual mode switch occurs.
Solution Approach 2:
The transition protocol dynamically adapts to individual driver characteristics and real-time contextual conditions. The system adjusts transition parameters such as duration, complexity of tasks, and monitoring intensity based on the specific driver's performance metrics and the current driving environment, making each transition customized rather than standardized.
2Reliability
If a customized transition plan is generated for each driver based on contextual factors and driver performance, then the reliability and safety of the transition process is improved, but the device complexity and computational requirements worsen
Solution Approach 1:
The transition planning system is segmented into modular components: driver performance assessment module, contextual factor analysis module, transition plan generation module, and real-time monitoring module. Each module handles a specific aspect of the customization process, making the overall complex system manageable and maintainable while still providing personalized transition plans.
Solution Approach 2:
The system automatically collects driver performance data through sensors and monitors contextual factors using onboard vehicles systems. The transition plan is self-generated based on this collected data, reducing the need for manual configuration or complex external processing, thereby managing system complexity while maintaining high reliability.
3Reliability
If the transition time is extended to ensure driver compliance and safety, then the reliability of the transition process is improved, but the productivity and efficiency of the driving operation deteriorates
Solution Approach 1:
Driver capability assessment and transition plan customization are performed in advance, before the actual mode transition begins. This preliminary preparation ensures that the transition can proceed efficiently without unnecessary delays, as the optimal duration and sequence of tasks are predetermined based on driver performance metrics.
Solution Approach 2:
The transition duration and complexity are dynamically adjusted based on real-time monitoring of driver response to transition tasks. If the driver demonstrates quick comprehension and compliance, the transition can be completed faster. The system continuously adapts the transition timeline to match the actual driver performance, optimizing both safety and efficiency.
Data Source
AI summary
Embodiments for implementing intelligent transition between autonomous and manual driving modes by a processor. A customized transition plan for one or more entities may be generated for transitioning between an autonomous driving mode and a manual driving mode according to one or more identified contextual factors and driver performance requirements. The customized transition plan may be applied for transitioning between the autonomous driving mode and the manual driving mode.