Dynamic Drivable Area Mapping for Construction Zone Navigation
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Solution Overview
Problem
Existing systems struggle to accurately predict future object trajectories and drivable areas in dynamic environments, leading to inefficiencies and safety risks in autonomous vehicle navigation, particularly in the presence of construction zones and temporary obstacles.
Innovation Solution
Utilizing machine learned models to analyze sensor and map data, generating top-down representations that identify drivable and non-drivable areas by inputting multi-channel images and polylines, allowing for more accurate and efficient determination of vehicle trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learned models are used to predict future object trajectories and drivable areas, then navigation safety is improved, but processing power and memory consumption increase
Solution Approach 1:
The system segments the environment into discrete drivable and non-drivable areas using grid-based representations. Machine learned models process sensor data to classify each grid cell independently, enabling parallel processing that reduces computational complexity while maintaining safety predictions for multiple objects simultaneously
Solution Approach 2:
The system performs preliminary classification of environmental areas as drivable or non-drivable before trajectory prediction. By pre-processing sensor data to identify static and dynamic obstacles, the system reduces the computational burden during real-time trajectory calculation, allowing faster decision-making with reduced processing power requirements
2Measurement precision
If machine learned models analyze sensor and map data to generate top-down representations, then drivable area identification accuracy is improved, but memory consumption increases
Solution Approach 1:
The system creates simplified top-down representations as abstract copies of the physical environment, using grid-based maps that store only essential drivability information. These compressed representations maintain high identification accuracy by focusing on relevant features while consuming minimal memory compared to storing complete sensor datasets
Solution Approach 2:
The system transforms three-dimensional sensor data into two-dimensional top-down representations, projecting spatial information onto a planar grid. This dimensional reduction preserves drivable area identification accuracy by maintaining spatial relationships while significantly reducing the memory required to store and process environmental data
3Reliability
If the system provides error checking and fallback options for sensor inaccuracies, then navigation reliability is improved, but device complexity increases
Solution Approach 1:
The system implements error checking and fallback mechanisms that are pre-configured but only activated when sensor inaccuracies are detected. By having backup processing paths ready in advance, the system maintains navigation reliability during sensor failures without requiring complex real-time decision-making about when to switch methods
Solution Approach 2:
The system continuously monitors sensor data quality and compares predictions against actual observations to detect inaccuracies. When errors are detected, feedback loops trigger fallback procedures that use alternative sensors or prediction methods, maintaining navigation reliability through adaptive error correction without requiring permanent complex error-handling infrastructure
Data Source
AI summary
Techniques for determining drivable area(s), parking location(s), or other incident areas in an environment are discussed herein. The drivable area(s), parking location(s), and/or other incident areas can be determined by a machine learned model. Training of the machine learned model can be based on sensor data and map data. The sensor data and the map data can be utilized to determine a representation (e.g., a top-down representation) of an environment. The representation can include at least road marking and velocity information associated with a dynamic object in the environment. The sensor data can be utilized to determine the dynamic object. The machine learned model can generate outputs including probabilities that elements of the outputs represent a drivable area, non-drivable area, a parking location, and/or an incident area. The outputs can be utilized to generate a trajectory. The trajectory can be utilized to control a vehicle to traverse the environment.