Vehicle Trajectory Analysis for Driving Environment Changes
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating environments where stored maps become outdated or inaccurate due to changes such as construction or accidents, leading to shifts in road lanes, which current systems struggle to detect and adapt to.
Innovation Solution
An autonomous vehicle system that monitors vehicle trajectories in real-time, compares them to expected trajectories based on stored maps, and determines environmental changes by calculating deviation metrics and probabilities using sensors and algorithms to update maps or switch to manual control when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous vehicle relies on stored maps for navigation, then the navigation system is simple and efficient, but the map data becomes outdated or inaccurate due to environmental changes such as construction or accidents
Solution Approach 1:
The system continuously monitors the actual trajectories of detected vehicles and compares them against the expected trajectories from stored maps. This feedback mechanism allows the system to detect deviations caused by environmental changes and trigger map updates, resolving the contradiction between navigation efficiency and map accuracy.
Solution Approach 2:
The autonomous vehicle system performs self-updating of maps by automatically detecting trajectory deviations and initiating map correction processes without external intervention. This enables the system to maintain accurate navigation data while continuing to operate autonomously.
2Reliability
If the autonomous vehicle continuously monitors and compares vehicle trajectories to detect environmental changes, then the map accuracy is maintained, but the computing power and processing time increase
Solution Approach 1:
The system performs trajectory monitoring and comparison operations selectively rather than continuously at full capacity. By triggering detailed analysis only when trajectory deviations exceed thresholds or when changes are detected, the system maintains map accuracy while reducing unnecessary computing energy consumption.
3Measurement precision
If the autonomous vehicle uses multiple sensors to detect vehicles and their trajectories, then the detection accuracy improves, but the device complexity increases
Solution Approach 1:
The system integrates data from multiple sensors (cameras, LIDAR, radar) to detect vehicles and determine their trajectories. By merging sensor inputs and processing them through a unified trajectory analysis system, the patent achieves high detection accuracy while managing complexity through integrated processing rather than separate systems for each sensor type.
Data Source
AI summary
A method and apparatus are provided for determining whether a driving environment has changed relative to previously stored information about the driving environment. The apparatus may include an autonomous driving computer system configured to detect one or more vehicles in the driving environment, and determine corresponding trajectories for those detected vehicles. The autonomous driving computer system may then compare the determined trajectories to an expected trajectory of a hypothetical vehicle in the driving environment. Based on the comparison, the autonomous driving computer system may determine whether the driving environment has changed and/or a probability that the driving environment has changed, relative to the previously stored information about the driving environment.