Vehicle Control Governor for Multi-Timestep Collision Avoidance
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Solution Overview
Problem
Existing advanced driver assistance systems (ADAS) lack an effective method to prevent or delay contact between vehicles by optimizing control inputs to maximize the number of timesteps where safety constraints are satisfied, particularly in scenarios involving rapid changes in target vehicle kinematics.
Innovation Solution
A computer system on the host vehicle determines an adjusted control input for components like brakes and steering systems to maximize the number of timesteps where constraints, such as maintaining a safe distance from a target vehicle, are satisfied by using a system dynamics model and recursive determination of feasible kinematic states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing ADAS control methods are used, then the system operates with standard control inputs, but the ability to prevent or delay contact between vehicles is insufficient
Solution Approach 1:
The system performs preliminary determination of adjusted control inputs by maximizing the number of timesteps where safety constraints are satisfied before actual control execution. This proactive optimization ensures collision prevention is prepared in advance, improving reliability without adding complex real-time computation during critical moments
Solution Approach 2:
The control optimization is segmented into discrete timesteps, where the system determines control inputs for each timestep independently to maximize constraint satisfaction. This segmentation allows the complex problem to be broken down into manageable time-based segments, making the system tractable while maintaining high collision prevention capability
2Reliability
If control inputs are optimized to maximize safety constraint satisfaction, then collision prevention is improved, but computational complexity increases
Solution Approach 1:
The system dynamically adjusts control inputs across multiple timesteps based on changing vehicle states and constraints. By formulating the problem as a dynamic optimization over time rather than a static solution, the system achieves high safety constraint satisfaction while maintaining computational tractability through the time-based dynamic structure
Solution Approach 2:
The patent replaces complex mechanical control trial-and-error methods with a computational optimization approach that directly calculates adjusted control inputs. This substitution uses mathematical optimization (maximizing timestep count where constraints are satisfied) rather than iterative mechanical adjustments, achieving high reliability with manageable computational complexity
3Loss of time
If the system maximizes timesteps for constraint satisfaction, then more time is available for collision prevention, but the control system requires more sophisticated adjustment mechanisms
Solution Approach 1:
The control system adjusts its own inputs autonomously by determining optimized control values that maximize constraint satisfaction across timesteps. This self-service capability allows the system to automatically extend the time available for collision prevention without requiring external intervention or complex additional adjustment mechanisms, as the optimization algorithm itself provides the necessary control refinement
Data Source
AI summary
A computer includes a processor and a memory, and the memory stores instructions executable by the processor to determine an adjusted control input for a component of a host vehicle that maximizes a number of timesteps for which a constraint on a host kinematic state of the host vehicle and a target kinematic state of at least one target vehicle is satisfiable; and actuate the component according to the adjusted control input. The determination of the adjusted control input is based on a nominal control input, the host kinematic state, and the target kinematic state.