Adaptive Crawl Control for Autonomous Vehicle Extrication
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Vehicles often become stuck in low-friction surfaces and adverse weather conditions, and existing systems lack the ability to effectively and autonomously extricate them using tailored solutions specific to the vehicle's parameters and conditions.
Innovation Solution
An AI-driven system that uses sensor data and crowd-sourced information to identify the type of stuck condition and apply tailored solutions, blending autonomous control with driver input to free the vehicle, taking into account vehicle-specific parameters such as make, model, and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing vehicle systems (Weather Mode, advanced all-wheel-drive) are used to address low-friction situations, then vehicle control in slippery conditions is improved, but the systems lack the ability to autonomously extricate stuck vehicles using tailored solutions
Solution Approach 1:
The vehicle system uses AI/ML models to autonomously analyze sensor data, determine stuck conditions, select appropriate extrication techniques, and control vehicle operations without requiring external assistance or complex driver intervention, enabling the system to service itself when stuck
Solution Approach 2:
The system dynamically adjusts multiple vehicle parameters including throttle position, transmission gear selection, brake application, and steering angle based on AI-determined extrication techniques, allowing tailored control strategies for different stuck conditions and vehicle configurations
2Reliability
If autonomous control takes over from the driver to apply learned extrication techniques, then the effectiveness of freeing the vehicle is improved, but the complexity of the control system increases
Solution Approach 1:
The AI/ML-based control system serves multiple functions: detecting stuck conditions, determining environmental factors, selecting extrication techniques, and controlling vehicle operations, consolidating these capabilities into a single multi-functional system rather than separate dedicated systems for each function
Solution Approach 2:
The system continuously monitors sensor data including wheel slip, vehicle motion, and environmental conditions, using this feedback to adjust control actions in real-time and refine extrication technique application based on actual vehicle response and changing conditions
3Measurement precision
If the system uses sensor data and crowd-sourced information to identify stuck conditions and apply tailored solutions, then the precision of extrication is improved, but the time required to process and apply the solution increases
Solution Approach 1:
The system pre-loads and maintains ready multiple extrication techniques and control strategies in its AI model, allowing it to quickly select and apply appropriate solutions without requiring time-consuming analysis or computation when a stuck condition occurs
Solution Approach 2:
The extrication process is divided into distinct segments: sensor data acquisition, AI analysis of stuck condition type, selection of appropriate technique, and execution of control actions, allowing parallel processing of independent tasks and reducing overall time through efficient task scheduling
Data Source
AI summary
Systems and methods for using autonomous vehicle assistance for freeing a vehicle from a stuck condition may include: receiving sensor data from a vehicle sensor indicating a condition of the vehicle; determining from the sensor data that the vehicle is in a stuck condition; obtaining a solution for freeing the vehicle from the stuck condition, wherein the technique is a learned solution developed based on collected data related to vehicle operator techniques for extrication; and taking over at least partial control of the vehicle from its operator and applying the learned technique to the vehicle.