Vehicle Obstacle Avoidance with Kinematic-Weighted Barrier Control
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Solution Overview
Problem
Existing advanced driver assistance systems (ADAS) face challenges in efficiently preventing collisions with obstacles by actuating vehicle components such as brakes or steering systems in a computationally efficient manner.
Innovation Solution
The system uses a control barrier function and a combination function, weighted based on the kinematic state of the obstacle, to determine a control input for the vehicle's components, thereby preventing obstacles from entering a buffer zone surrounding the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing ADAS collision prevention methods are used, then collision avoidance function is provided, but computational efficiency is insufficient
Solution Approach 1:
The collision prevention problem is segmented into two independent components: a control barrier function that ensures safety constraints are met, and a combination function that optimizes control inputs. This segmentation allows each component to be computed separately and efficiently, improving overall computational performance while maintaining collision prevention reliability.
Solution Approach 2:
The system changes parameters dynamically by adjusting weights in the combination function based on the kinematic state of obstacles. This allows the control strategy to adapt to different scenarios (e.g., stationary vs. moving obstacles, different distances), improving computational efficiency by focusing resources on the most relevant constraints for each situation.
2Measurement precision
If computationally intensive methods are used to determine control inputs, then collision prevention accuracy is improved, but processing speed decreases
Solution Approach 1:
The control barrier function is formulated in advance to encode safety constraints and collision prevention requirements. By pre-defining the mathematical structure and constraints, the system avoids performing complex optimization calculations in real-time, thus improving processing speed while maintaining control input accuracy through the structured combination function.
3Productivity
If simple control methods are used, then computational efficiency is improved, but collision prevention reliability deteriorates
Solution Approach 1:
The combination function incorporates feedback from the control barrier function and obstacle kinematic states to dynamically adjust control inputs. This feedback mechanism ensures that even with computationally efficient methods, the system maintains high collision prevention reliability by continuously adapting control decisions based on current system state and constraint satisfaction.
Data Source
AI summary
A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive sensor data indicating an obstacle, formulate a control barrier function for a vehicle and the obstacle based on the sensor data, determine a control input based on the control barrier function and a combination function, and actuate a component of the vehicle according to the control input. The combination function is a sum of a first function weighted by a first weight and a second function weighted by a second weight, and the first weight and the second weight are based on a kinematic state of the obstacle.