Autonomous Vehicle Path Planning Using Leading Vehicle Sensor Data
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Solution Overview
Problem
Autonomous vehicles often fail to adequately account for unexpected obstacles like speed bumps, potholes, and transient objects during path planning, leading to potential navigation issues.
Innovation Solution
The system includes a path planning method that uses sensor data from leading vehicles to establish a modified path and classifies obstacles using a processor, allowing the autonomous vehicle to adjust its route based on observed behavior and nature of the obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses a standard path planning system, then the navigation is simple and fast, but the vehicle cannot adequately account for unexpected obstacles like speed bumps, potholes, and transient objects
Solution Approach 1:
The system performs preliminary path planning to establish an initial path, then prepares for obstacle detection and path modification before actually encountering obstacles. This allows the system to have a ready-made plan while maintaining flexibility to adapt when obstacles are detected, resolving the contradiction between simple navigation and reliable obstacle detection.
Solution Approach 2:
The path planning system dynamically modifies the initially established path based on detected obstacles. The system transitions from a static path to a dynamic modified path that avoids obstacles while still reaching the destination, thereby improving reliability without requiring the entire system to be complex from the start.
2Reliability
If the autonomous vehicle modifies its path based on leading vehicle behavior, then the navigation safety improves, but the processing time and computational load increase
Solution Approach 1:
The system uses sensor data from leading vehicles as feedback to modify its own path. By continuously monitoring leading vehicle positions and behaviors, the system can make real-time path adjustments without requiring complex predictive modeling, thus improving navigation safety while keeping processing time manageable through efficient feedback utilization.
3Measurement precision
If the autonomous vehicle classifies obstacles in real-time, then the obstacle avoidance accuracy improves, but the computational requirements and processing time increase
Solution Approach 1:
The system applies obstacle classification primarily to detected obstacles that require path modification, rather than classifying all objects in the environment. This localized approach to classification maintains high accuracy for critical obstacles while reducing overall computational power requirements by focusing processing resources only where needed.
Data Source
AI summary
Systems and method are provided for controlling a vehicle. A path planning method for a vehicle includes establishing a first path for a first vehicle, then receiving, at the first vehicle, sensor data associated with a behavior of one or more leading vehicles observed in the vicinity of the first vehicle. The method further includes establishing a modified path that diverges from the first path based on the sensor data, then classifying, with a processor, an obstacle observed by the first vehicle as it travels along the modified path.