Off-Road Trace Guidance for Learning Vehicle Parameter Matching
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Solution Overview
Problem
Learning vehicle drivers face challenges in navigating off-road trails due to the lack of available data and the complexity of adjusting driving parameters without a precedent driver's guidance.
Innovation Solution
A control system for a learning vehicle that receives a trace of an off-road trail from a precedent vehicle, adjusts driving parameters based on trace parameters, and provides instructions through audio, visual, or haptic formats to guide the learning vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a learning vehicle driver manually adjusts every driving parameter without precedent guidance, then the driver can navigate off-road trails independently, but the complexity of operation increases significantly and success rate decreases
Solution Approach 1:
The system creates a digital trace of the precedent vehicle's path and driving parameters, allowing the learning vehicle to copy the exact trajectory and settings. This eliminates the need for the learning driver to manually figure out complex parameter adjustments, as they can simply follow the recorded trace and replicate successful driving decisions.
Solution Approach 2:
The precedent vehicle performs all the complex parameter adjustments and pathfinding actions beforehand, recording the optimal sequence of driving decisions. This preliminary action captures the expertise and trial-and-error process, so the learning vehicle can benefit from pre-computed optimal parameters without experiencing the complexity firsthand.
2Loss of information
If off-road trail data is extensively recorded and shared, then learning drivers can benefit from precedent traces, but the quantity of data to process and manage increases
Solution Approach 1:
The system extracts only the essential elements needed for learning - the trace path coordinates and key driving parameters - while discarding redundant information such as unnecessary sensor readings, timing data, or duplicate measurements. This extraction focuses the data into a manageable format that retains all critical learning value without the bulk of raw data.
Solution Approach 2:
The system transforms raw trace data into standardized parameter formats that are easier to process and compare. By normalizing parameters like speed, steering angle, and throttle position into consistent units and representations, the system reduces data complexity while maintaining all essential information for learning purposes.
3Productivity
If the control system automatically adjusts driving parameters based on trace parameters, then the learning vehicle can traverse trails more effectively, but the extent of automation increases system complexity
Solution Approach 1:
The automation system is divided into modular components: trace reading module, parameter extraction module, parameter adjustment module, and monitoring module. Each segment handles a specific aspect of the automation process, making the overall complex system manageable through functional decomposition. This segmentation allows selective activation of automation features based on trail difficulty and driver skill level.
Data Source
AI summary
Particular embodiments may provide a virtual off-road guide for a learning vehicle. In some embodiments, a control system for a learning vehicle comprises one or more processors and a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to receive a trace of an off-road trail traversed by a precedent vehicle and corresponding trace parameters; in response to receiving the trace parameters, adjusting one or more driving parameters of the learning vehicle based on the trace parameters; and providing instructions for traversing the trace based on the trace parameters. In some embodiments, the trace parameters comprise location, orientation, relative position along the off-road trail, ride height, drive mode, brake regeneration level, steering angle, power consumption, acceleration, or torque.