Collision Course Prediction Using Segmented Steering and Braking Algorithms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing collision course prediction systems lack reliability in steering intervention during potential collision situations, requiring significant computer processing capacity and struggling to accurately differentiate between colliding and non-colliding objects.
Innovation Solution
A system that includes a sensor system to measure target vehicle position, velocity, and acceleration, a time-to-collision estimator, and a collision course condition determination unit, which calculates time-to-collision and lateral distance using Kalman filters to assess collision likelihood and generate interventions such as steering and braking, while considering driver override and legal limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex decision algorithms and multiple sensors are used for collision avoidance, then collision detection capability is improved, but computer processing capacity requirements increase
Solution Approach 1:
The patent segments the collision detection task into two distinct algorithms: a steering algorithm that processes lateral collision risks using sensor data with lateral position information, and a braking algorithm that processes longitudinal collision risks using sensor data with longitudinal position information. This segmentation allows each algorithm to operate with reduced computational complexity while maintaining overall collision detection reliability
Solution Approach 2:
The system dynamically selects which algorithm to execute based on the type of collision risk detected. The steering algorithm is activated when lateral collision risk is identified, while the braking algorithm is activated for longitudinal collision risks. This dynamic selection optimizes processing capacity by only activating the necessary algorithm for each situation
2Reliability
If steering intervention is applied to avoid collision, then collision avoidance capability is improved, but system complexity increases
Solution Approach 1:
The patent divides the collision avoidance system into separate steering and braking control pathways. Each pathway has its own decision algorithm and actuator control, allowing independent optimization and simplification of each subsystem while maintaining overall system effectiveness
Solution Approach 2:
The patent introduces an intermediary processing layer that receives sensor data and determines the appropriate collision avoidance strategy before activating steering or braking interventions. This intermediary layer simplifies the overall system architecture by centralizing the decision-making logic
Data Source
AI summary
A system for collision course prediction includes a host vehicle sensor system detecting information relating to a target object including a position and a velocity relative to the host vehicle; a time-to-collision estimator control block calculating an estimated time-to-collision based on a longitudinal distance, longitudinal velocity and longitudinal acceleration of the target object relative to the host vehicle; a lateral distance estimator control block, which estimates the lateral distance between the centers of the host vehicle and target object at the estimated time-to-collision; and a collision course condition determination unit determining, at a determination instant prior to the estimated time-to-collision, a probability that the host vehicle will collide with the target object dependent at least in part upon whether the lateral distance is within a first interval, the first interval based on at least a lateral width of the host vehicle and a lateral width of the target object.