Autonomous Vehicle Lane Change Prediction from Wheel Orientation
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Solution Overview
Problem
Autonomous vehicles face difficulties in accurately predicting the future location and direction of human-driven vehicles, particularly when they exhibit unpredictable behavior such as sudden lane changes, which can lead to close cut-ins and pose navigation challenges.
Innovation Solution
An autonomous vehicle system that uses sensor signals from multiple systems (e.g., lidar, image, radar) to compute angles, misalignments, and eccentricities of other vehicles' wheels to predict impending lane changes, allowing for pre-emptive control of its mechanical systems to adjust position or alert the human driver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle uses traditional sensor-based detection methods, then it can detect objects in the environment, but it cannot accurately predict sudden lane changes by human-driven vehicles
Solution Approach 1:
The system performs preliminary detection of wheel orientation and vehicle posture before the actual lane change occurs. By analyzing the angle between wheels and vehicle body in advance, the system predicts imminent lane changes and prepares appropriate responses, transforming reactive detection into proactive prediction.
Solution Approach 2:
The system adds a new dimension of analysis by detecting wheel orientation angles and vehicle body misalignment, which are not captured by traditional object detection. This additional angular dimension enables the system to distinguish between normal driving and imminent lane change maneuvers.
2Speed
If the autonomous vehicle reacts quickly to close cut-ins, then it can respond to sudden lane changes, but it cannot predict the lane change before it happens
Solution Approach 1:
The system performs preliminary detection of wheel orientation and vehicle posture before the actual lane change occurs. By analyzing the angle between wheels and vehicle body in advance, the system predicts imminent lane changes and prepares appropriate responses, transforming reactive detection into proactive prediction.
Solution Approach 2:
The system prepares compensatory actions in advance by detecting early signs of lane change intent through wheel and body angle analysis. This allows the autonomous vehicle to pre-position itself or alert the driver before the actual cut-in occurs, cushioning against the sudden maneuver.
3Measurement precision
If the autonomous vehicle monitors wheel angles and body misalignment, then it can predict lane changes, but the system complexity increases
Solution Approach 1:
The system extracts only the critical features needed for lane change prediction: wheel orientation angles and vehicle body misalignment. By focusing on these specific geometric parameters rather than analyzing all sensor data, the system achieves accurate prediction with reduced computational complexity.
Solution Approach 2:
The system transforms complex sensor data into simplified angular parameters (wheel angle relative to vehicle body). This parameter transformation reduces the dimensionality of the problem and enables efficient real-time computation while maintaining prediction accuracy.
Data Source
AI summary
An autonomous vehicle is configured to estimate a change in direction of a vehicle that is on a roadway and is proximate to the autonomous vehicle. The autonomous vehicle has a mechanical system, one or more sensors that generate one or more sensor signals, and a computing system in communication with the mechanical system and the one or more sensors. The autonomous vehicle is configured to detect an imminent lane change by another vehicle based on at least one of a computed angle between a wheel of the other vehicle and a longitudinal direction of travel of the other vehicle, a degree of misalignment between the wheel of the other vehicle and a body of the other vehicle, and/or an eccentricity of the wheel of the other vehicle. The mechanical system of the autonomous vehicle is controlled by the computing system based upon the detected imminent lane change.