Driver Takeover Recommendations Using Personalized Driving Prediction
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Solution Overview
Problem
Existing driver assistance systems in vehicles are underutilized due to drivers' reluctance to engage automated driving functions, often due to loss of control, lack of trust, or desire for driving pleasure, leading to missed opportunities for increased safety, efficiency, and comfort.
Innovation Solution
A vehicle system with a recommendation module that uses data collection, prediction, and machine learning to tailor automated driving recommendations based on individual driver profiles, predicting when the driver would prefer manual or automated control, and issuing personalized suggestions to enhance acceptance and usage of automated features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driver assistance systems are provided with automated driving functions, then safety and comfort are improved, but drivers' reluctance to engage automated functions due to loss of control and lack of trust reduces utilization
Solution Approach 1:
The system continuously monitors driver state through cameras and sensors, providing real-time feedback about when automated functions are available and appropriate. The system adapts its recommendations based on driver responses, creating a feedback loop that builds trust while maintaining safety.
Solution Approach 2:
The recommendation system acts as an intermediary between the driver and automated driving functions. Instead of directly controlling the vehicle, it provides personalized suggestions that guide drivers toward using automated functions when appropriate, bridging the gap between driver reluctance and system capabilities.
2Productivity
If automated driving functions are made easily accessible, then usage proportion is increased, but drivers may lose driving pleasure and sense of control
Solution Approach 1:
The system dynamically adjusts its recommendations based on real-time driver state, vehicle context, and route characteristics. Rather than statically enabling automated functions, it adaptively suggests them when appropriate, allowing drivers to maintain control when desired while using automation when beneficial.
Solution Approach 2:
The system predicts upcoming driving situations and proactively recommends automated functions before the driver would naturally take over. By anticipating monotonous or challenging road sections ahead, it suggests automation in advance, allowing drivers to prepare mentally while maintaining agency over the decision.
3Ease of operation
If manual activation is required for driver assistance functions, then driver control is maintained, but manual checking and activation increases time and effort
Solution Approach 1:
The system performs self-assessment of driving conditions and automatically generates personalized recommendations without requiring manual driver input. Drivers simply receive tailored suggestions based on their preferences and the current context, eliminating the need for manual checking while preserving ultimate driver control.
Data Source
AI summary
A vehicle has a recommendation system, which includes a data collection module, a prediction module, and a recommendation module. The data collection module collects vehicle data, surroundings data, and/or environmental data. The prediction module reads a driver profile from a multitude of driver profiles, each driver profile including a machine learning model trained specifically for the respective driver profile set up to read the vehicle data, surroundings data, and/or environmental data at least for a route portion lying ahead and to issue a predictive indication value as an output variable. The recommendation module compares the predictive indication value to an indication threshold value and prompts a recommendation to be issued for a person driving the vehicle or a driver assistance system to take over vehicle control depending on the position of the predictive indication value in relation to the indication threshold value.