Automated Vehicle Longitudinal Control Using CIP Path Divergence
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Solution Overview
Problem
Existing vehicle control systems struggle to accurately track and maintain the closest-in-path (CIP) vehicle during complex transient maneuvers, leading to suboptimal longitudinal control performance in automated acceleration and braking scenarios due to rapid changes in vehicle states.
Innovation Solution
A vehicle control module that utilizes path history comparison and divergence index calculation to determine whether to track or drop the CIP vehicle, incorporating environmental context and vehicle state variables to improve tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If object tracking sensors are used to identify CIP vehicle, then automated vehicle control can be achieved, but during complex transient maneuvers the tracking accuracy deteriorates and the system cannot appropriately select, hold, or drop the CIP vehicle
Solution Approach 1:
The patent segments the target vehicle selection process into multiple independent evaluation dimensions: lateral position deviation, longitudinal distance, divergence index, and path history consistency. Each dimension is evaluated separately and combined to determine the optimal CIP vehicle, improving tracking accuracy during complex maneuvers by avoiding reliance on a single metric
Solution Approach 2:
The system dynamically adjusts the CIP vehicle selection criteria based on real-time maneuver characteristics. During complex transient maneuvers, the divergence index calculation adapts to quickly changing states of both host and target vehicles, allowing the system to appropriately select, hold, or drop CIP vehicles based on current driving conditions rather than fixed thresholds
2Reliability
If the system tracks target vehicle movement to control acceleration and braking, then longitudinal control performance improves, but during complex transient maneuvers the control performance deteriorates due to suboptimal tracking
Solution Approach 1:
The system continuously calculates the divergence index based on real-time path history comparison between host and target vehicles. This feedback mechanism allows the control system to detect when the target vehicle is deviating from the host vehicle's path and adjust tracking accordingly, maintaining reliable longitudinal control during complex transient maneuvers by continuously adapting to changing conditions
Solution Approach 2:
The patent changes the evaluation parameters dynamically based on maneuver complexity. The divergence index incorporates lateral position error and heading angle differences, and the system adjusts the weighting and thresholds of these parameters according to the current driving scenario, enabling reliable control performance across both normal and complex transient maneuvers
Data Source
AI summary
A method for controlling automated vehicle acceleration and braking includes detecting, via at least one vehicle object sensor of a host vehicle, a closest-in-path (CIP) target vehicle ahead of the host vehicle, obtaining a path history of the CIP target vehicle, the path history including multiple historic positions of the CIP target vehicle at specified time intervals, obtaining a path history of the host vehicle, the path history of the host vehicle including multiple historic positions of the host vehicle at the specified time intervals, comparing the path history of the CIP target vehicle to the path history of the host vehicle to determine a divergence index, and in response to the divergence index being less than a specified divergence threshold, controlling automated acceleration and braking of the host vehicle based at least in part on tracked movement of the CIP target vehicle.