Vehicle Control Strategy With Probabilistic Path and Actuator Guidance
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Solution Overview
Problem
Existing vehicle control systems lack the ability to optimize performance and respond effectively to emergency events during high-performance driving, such as on a racetrack, due to the complexity of variables and unpredictable conditions.
Innovation Solution
A vehicle system that includes a prediction and user interaction module to determine optimal actuator inputs, monitor vehicle and user inputs, and predict emergency events, presenting suggested actuator commands to enhance performance and response to emergencies through data fusion and probabilistic path prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a system uses multiple sensors and perception devices to improve vehicle awareness and autonomous control capability, then the vehicle's autonomous control performance is improved, but the device complexity increases
Solution Approach 1:
The system segments the complex control task into multiple independent modules: optimization module for control strategy determination, monitoring module for real-time data collection, prediction module for emergency event forecasting, and interface module for driver interaction. Each module processes specific aspects of vehicle control independently, reducing overall system complexity while maintaining high autonomous control capability.
Solution Approach 2:
The patent introduces an optimization module as an intermediary between the driver and vehicle actuators. This module receives driver inputs, processes them through probabilistic predictions and control strategies, then outputs optimized actuator commands. The intermediary handles the complexity of coordinating multiple sensors and control systems, shielding the driver from system complexity while enabling advanced autonomous control functions.
2Productivity
If the system provides comprehensive real-time optimization and emergency response capabilities, then vehicle performance is enhanced, but the computational requirements and processing time increase
Solution Approach 1:
The prediction module performs preliminary actions by continuously forecasting potential emergency events before they occur. It uses probabilistic predictions to identify likely scenarios in advance, allowing the optimization module to pre-calculate control strategies. This proactive approach enables the system to respond to emergencies more quickly, reducing actual reaction time despite comprehensive analysis.
Solution Approach 2:
The system applies partial action by focusing computational resources on the most probable emergency scenarios rather than analyzing all possible events equally. The prediction module identifies high-probability threats and directs optimization efforts toward those specific situations, achieving effective emergency response with reduced computational overhead and faster processing times.
3Measurement precision
If the system presents detailed actuator commands to the driver for acceptance, then the driver's understanding and control precision are improved, but the driver's cognitive load and response time may increase
Solution Approach 1:
The interface module provides continuous feedback to the driver by presenting suggested actuator commands and their expected outcomes. The system monitors driver acceptance decisions and adjusts future suggestions based on driver preferences and performance. This feedback loop maintains high control precision while adapting to individual driver capabilities, reducing cognitive load over time as drivers become familiar with system suggestions.
Solution Approach 2:
The system dynamically changes the level of detail and complexity of presented commands based on driving conditions and driver behavior. In normal conditions, it provides detailed suggestions for precision control. During high-stress or emergency situations, it simplifies presentations to reduce cognitive load and enable faster driver response, maintaining control precision through adaptive information presentation.
Data Source
AI summary
A system for interacting with a vehicle user includes an optimization module configured to determine a control strategy for traversing a selected route based on a probabilistic prediction of actuator inputs, a monitoring module configured to monitor vehicle motion and driver inputs during operation of a vehicle over the selected route, and a prediction module configured to predict an optimal local path for traversing a section of the selected route, and predict a set of optimal actuator control actions. The system also includes an interface module configured to present a suggested set of actuator commands to the vehicle user based on the set of optimal actuator control actions, and a control system configured to control the vehicle to execute the suggested set of actuator commands based on the user accepting the suggested set of actuator commands.