Autonomous Vehicle Driving Mode Control for User-Adaptive Trips
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Solution Overview
Problem
Existing autonomous driving systems fail to adapt to individual user preferences and expectations, applying the same control logic across different driving scenarios, thus failing to optimize user experience.
Innovation Solution
An autonomous vehicle system that selects an appropriate driving mode based on user input and historical traffic information, dynamically adjusting driving style to minimize the difference between actual and historical trip completion times, using a control unit that integrates human-machine interaction, communication with external devices, and sensor data to optimize user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the same control logic is applied to all driving scenes, then the system is simple and reliable, but it cannot adapt to different user needs and expectations
Solution Approach 1:
The patent implements dynamic driving styles that can switch between multiple pre-defined modes (conservative, moderate, aggressive) based on user preferences and real-time conditions. The control logic transitions from static to dynamic by allowing the autonomous vehicle to adapt its driving behavior characteristics including acceleration, deceleration, and lane changing strategies according to user-selected styles
Solution Approach 2:
The system changes control parameters such as acceleration thresholds, deceleration rates, and lane changing timing based on the selected driving style. Each driving style corresponds to a set of optimized parameters that balance safety, comfort, and efficiency, allowing the same vehicle to exhibit different behavioral characteristics without requiring fundamentally different control architectures
2Productivity
If historical traffic information is used to optimize trip time, then travel efficiency improves, but the system requires more data processing and communication resources
Solution Approach 1:
The system performs preliminary data collection and analysis by gathering historical traffic information before the actual trip. The autonomous vehicle queries historical average times for different road segments and uses this pre-acquired data to plan the optimal driving style selection, rather than making decisions in real-time during the trip
Solution Approach 2:
The system establishes a feedback loop where the autonomous vehicle compares its actual trip progress against historical average times for the same route segments. Based on this comparison, the system dynamically adjusts the driving style to either catch up or maintain pace with historical benchmarks, creating a closed-loop control system that continuously optimizes trip completion time
Data Source
AI summary
A method for controlling an autonomous vehicle including: obtaining trip information of a current trip of a user of the autonomous vehicle and historical traffic information related to the current trip; selecting an autonomous driving (AD) mode that is suitable for the current trip from a plurality of pre-defined AD modes; comparing time required for the autonomous vehicle to complete a portion of the current trip in the selected AD mode with historical average time to complete the portion of the current trip, the historical average time being acquired based on the historical traffic information; and dynamically adjusting driving of the autonomous vehicle based on the comparison to minimize the difference between the time required for the autonomous vehicle to complete the portion of the current trip in the selected AD mode and the historical average time.