Autonomous Vehicle Projection Footprint for Geofence Boundary Avoidance
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Solution Overview
Problem
Autonomous and semi-autonomous driving solutions for heavy equipment face challenges in maintaining spatial awareness and preventing unintended boundary intersections, leading to unnecessary speed reductions or unsafe deceleration when approaching boundaries.
Innovation Solution
The method generates a two-dimensional footprint for the vehicle and its implements based on current position, orientation, and steering state, predicting future spatial occupancy and adjusting speed to maintain a safe distance from boundaries, allowing for timely and safe stops without unnecessary deceleration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle maintains spatial awareness using traditional boundary detection methods, then boundary intersection prevention is achieved, but unnecessary speed reductions and unsafe deceleration occur when approaching boundaries
Solution Approach 1:
The system performs preliminary action by generating a projection footprint that predicts future vehicle position and orientation before the vehicle actually reaches boundary zones. By calculating where the vehicle will be based on current trajectory, orientation, and dimensional data, the system takes preventive action in advance rather than reacting to current position, thereby avoiding unnecessary speed reductions while maintaining reliable boundary intersection prevention
Solution Approach 2:
The system applies dynamics by continuously updating the projection footprint based on real-time changes in vehicle position, orientation, and dimensional characteristics. The footprint is dynamically regenerated as the vehicle moves, allowing the boundary detection system to adapt to changing vehicle states and maintain operational efficiency through precise, context-aware monitoring rather than static boundary approaches
2Device complexity
If the autonomous vehicle uses a static boundary detection approach, then simple boundary monitoring is implemented, but the vehicle cannot account for changes in orientation and dimensional characteristics
Solution Approach 1:
The system transitions from two-dimensional boundary lines to three-dimensional projection footprints that incorporate vehicle length, width, height, and orientation. By adding dimensional depth to the detection model, the system maintains relatively simple boundary comparison logic while dramatically improving measurement precision through volumetric spatial awareness that accounts for vehicle attitude and dimensional characteristics
Solution Approach 2:
The system creates a virtual copy of the vehicle in the form of a projection footprint that replicates the vehicle's dimensional characteristics and spatial occupancy. This digital twin approach allows complex spatial reasoning to be performed on the footprint representation rather than directly on the physical vehicle, maintaining detection system simplicity while achieving high measurement precision through accurate virtual modeling
Data Source
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AI summary
Techniques for autonomous vehicle boundary intersection detection and avoidance are described. In an example, a geofence boundary is received at a display coupled to a control module of a vehicle. A 2D footprint is generated using a definition of the vehicle and an implement coupled to the vehicle. Using geographic coordinates for the vehicle, a current position and orientation for the footprint are determined. A 2D projection footprint is generated for the vehicle using the current position and orientation, a current steering state, and a direction of travel. A first distance from the current position and orientation at which the projection footprint intersects with the boundary is determined. Based on the first distance, the speed of the vehicle is maintained at or below a maximum speed.