Autonomous Lane Change Control via Dynamic State Prediction
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Solution Overview
Problem
Conventional autonomous vehicle control systems fail to accurately predict collisions with dynamic obstacles, particularly when interacting with other proximate dynamic vehicles, leading to unsafe and uncomfortable lane change maneuvers.
Innovation Solution
A system and method for automated lane change control that considers the positions, headings, speed, and acceleration of proximate dynamic vehicles, using sensors and a computing device to generate a lane change trajectory, defining safety distances, and performing the lane change in two phases: longitudinal positioning and lateral steering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional autonomous vehicle control systems use polynomial expressions to represent spatial information for avoiding stationary obstacles, then the vehicle can perform basic path following, but the system cannot accurately predict future positions of dynamic vehicles leading to collision risks
Solution Approach 1:
The system transitions from static polynomial path representation to dynamic state prediction that continuously updates based on current positions, velocities, and accelerations of all vehicles. The prediction model dynamically adjusts to changing traffic conditions while maintaining computational efficiency through structured state representations.
Solution Approach 2:
A state prediction model is introduced as an intermediary component between sensor data and control decisions. This model processes raw sensor information about proximate vehicles and generates predicted future states, serving as a bridge that enables accurate collision prediction without requiring direct complex calculations between all system components.
2Reliability
If the autonomous vehicle performs lane change maneuvers without considering proximate dynamic vehicles, then the maneuver execution is simple and quick, but the lane change may be unsafe and uncomfortable
Solution Approach 1:
The system performs preliminary prediction of proximate vehicle positions and trajectories before initiating the lane change maneuver. By anticipating the behavior of nearby vehicles in advance, the system can plan safe lane change trajectories that account for dynamic obstacles, ensuring safety while maintaining efficient execution through pre-computed paths.
3Measurement precision
If the system considers positions, speed, and acceleration of proximate dynamic vehicles using state prediction models, then accurate collision prediction is achieved, but the computational complexity and processing requirements increase
Solution Approach 1:
The prediction system segments the problem by treating each proximate vehicle independently with its own state vector (position, velocity, acceleration). This segmentation allows the system to manage computational complexity by processing individual vehicle predictions separately rather than solving a single complex multi-vehicle interaction problem, while still achieving accurate overall prediction.
Data Source
AI summary
A system and method for automated lane change control for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; use the perception data to determine a state of the host vehicle and a state of proximate vehicles detected near to the host vehicle; determine a first target position within a safety zone between proximate vehicles detected in a roadway lane adjacent to a lane in which the host vehicle is positioned; determine a second target position in the lane in which the host vehicle is positioned; and generate a lane change trajectory to direct the host vehicle toward the first target position in the adjacent lane after directing the host vehicle toward the second target position in the lane in which the host vehicle is positioned.