Vehicle Driving Assistance Planning Based on Occupant Attentiveness
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Solution Overview
Problem
Autonomous vehicle driving behavior planning becomes increasingly complex with longer planning periods and numerous behavioral alternatives, leading to high computational demands that can compromise driving comfort and safety.
Innovation Solution
A method that adapts driving behavior planning parameters based on the attentiveness of vehicle occupants, using sensors to detect their state and adjust computing resources, allowing for the release of free computing capacity to other vehicle functions, thereby optimizing decision-making and reducing complexity without compromising comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving behavior planning considers more behavioral alternatives and extends planning periods, then driving comfort and safety are improved, but computing capacity requirements increase
Solution Approach 1:
The system dynamically adjusts the planning horizon and number of behavioral alternatives based on the detected attentiveness state of vehicle occupants. When inattentiveness is detected, the planning period is reduced and fewer alternatives are evaluated, thereby reducing computing capacity requirements while maintaining safety through adaptive parameter adjustment
Solution Approach 2:
The system changes key parameters of the driving behavior planning (planning period duration, number of behavioral alternatives to evaluate) based on the attentiveness state. This parameter adaptation allows the system to reduce computational load when full planning complexity is not necessary for safety, thus resolving the contradiction between comprehensive planning and computing resource consumption
2Reliability
If the planning tree includes more behavioral alternatives, then driving comfort is improved, but device complexity increases
Solution Approach 1:
The system dynamically adapts the complexity of the behavior tree by adjusting the number of alternatives evaluated at each node based on occupant attentiveness. When occupants are inattentive, the system reduces the number of alternatives considered, thereby reducing planning complexity while maintaining sufficient driving comfort through adaptive simplification
3Reliability
If computing capacity is increased to handle complex planning, then driving safety is improved, but hardware cost and power requirements increase
Solution Approach 1:
The system uses sensor apparatus already present in the vehicle to detect occupant attentiveness and automatically adjusts planning parameters accordingly. This self-service mechanism allows the system to reduce computing capacity requirements without external intervention, thereby reducing power consumption and hardware cost requirements while maintaining safety through intelligent adaptation
Data Source
AI summary
A method for operating a vehicle. The method includes reading an occupant signal, via an interface from at least one sensor apparatus of the vehicle, which represents a behavior of at least one vehicle occupant detected by the at least one sensor apparatus. A state signal is ascertained using the occupant signal. The state signal represents at least one state of attentiveness of the at least one vehicle occupant. At least one parameter of driving behavior planning for the vehicle is adapted depending on the state signal. A computing capacity required for the adapted driving behavior planning is determined in order to generate a capacity signal. The capacity signal represents a free computing capacity which corresponds to a difference between the required computing capacity and an overall available computing capacity. The free computing capacity is released to execute a function of the vehicle, depending on the capacity signal.