Autonomous Vehicle Intent Prediction for Lane-Change Collision Avoidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles struggle to accurately predict the intent of surrounding vehicles, particularly when they intend to change lanes, leading to potentially unsafe and unnatural driving maneuvers due to uncertainty and data noise in perception systems.
Innovation Solution
An autonomous vehicle system uses sensors and machine learning models to monitor the lateral position and velocity of nearby vehicles, predicting their trajectory and intent to change lanes, and adjusts its path to avoid collisions by determining an alternative trajectory if a likelihood of collision is detected within a threshold time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle uses perception systems to monitor surrounding vehicles, then it can detect lateral position and velocity data, but uncertainty and data noise lead to inaccurate intent prediction
Solution Approach 1:
The system performs preliminary classification of vehicles into 'cut-in' or 'exit' intent categories before making trajectory adjustments. By pre-classifying vehicle intent based on monitored lateral position and velocity data, the system prepares predictive models in advance, reducing reaction time and improving prediction accuracy despite data noise
Solution Approach 2:
The system introduces an intermediary classification layer between raw sensor data and trajectory control. This intermediary step categorizes vehicle intent (cut-in vs. exit) before feeding into predictive models, filtering out noise and uncertainty from raw perception data to improve final prediction reliability
2Reliability
If the autonomous vehicle reacts to potential lane changes, then it can avoid collisions, but it may exhibit unnatural and uncomfortable driving maneuvers
Solution Approach 1:
The system classifies vehicle intent in advance (cut-in vs. exit) before executing trajectory adjustments. This preliminary classification allows the autonomous vehicle to anticipate and smoothly adapt to human driver intentions, reducing abrupt and unnatural maneuvers while maintaining collision avoidance
Solution Approach 2:
The system dynamically adjusts trajectory based on classified vehicle intent rather than using fixed reaction patterns. By making the trajectory control adaptive to the specific intent classification, the system achieves more natural and comfortable driving behavior while maintaining safety
3Measurement precision
If the autonomous vehicle monitors lateral position and velocity of nearby vehicles, then it can predict trajectory, but the complexity of analyzing multiple parameters increases computational load
Solution Approach 1:
The system segments the complex prediction task into distinct stages: first monitoring lateral position and velocity, then classifying intent (cut-in or exit), and finally predicting trajectory. This segmentation of the computational process reduces overall complexity by breaking down the analysis into manageable, sequential steps
Solution Approach 2:
The system performs preliminary intent classification based on monitored parameters before executing full trajectory prediction. By pre-classifying vehicle intent, the system reduces the computational scope of subsequent prediction tasks, lowering overall computational load while maintaining accuracy
Data Source
AI summary
A method comprises periodically monitoring, by a processor, lateral position and velocity of vehicle within a predetermined distance from an autonomous vehicle, the vehicle moving in a direction having at least one common attribute with the autonomous vehicle; executing, by the processor, a computer model using the monitored lateral position and velocity of the vehicle, to predict whether a trajectory for the vehicle; and when a current trajectory of the autonomous has a likelihood of collision with the predicted trajectory of the vehicle that satisfies a threshold, determining, by the processor, an alternative trajectory for the autonomous vehicle.