Parallel ML Motion Planning for Real-Time Vehicle Control
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Solution Overview
Problem
Conventional machine learning models for controlling vehicles execute modules in a serial manner, leading to reduced speed and generating erroneous planned motions that can be dangerous, impacting real-time performance and safety.
Innovation Solution
Implementing a parallelized machine learning model with cross-attention features that allows multiple modules to execute simultaneously, processing sensor data to generate planned motions for vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If modules execute in serial order, then the model structure is simple and easy to implement, but the execution speed is reduced and real-time performance is impacted
Solution Approach 1:
The machine learning model is segmented into multiple independent modules (mapping module, motion prediction module, occupancy prediction module, planning module) that can execute in parallel. Each module processes specific aspects of sensor data independently, allowing simultaneous execution without requiring sequential completion, thereby increasing execution speed while maintaining manageable complexity through modular design.
Solution Approach 2:
The model transitions from one-dimensional sequential execution to multi-dimensional parallel execution by introducing cross-attention mechanisms that allow modules to interact across different dimensions. This dimensional transformation enables simultaneous processing while maintaining necessary inter-module communication, resolving the contradiction between speed and complexity.
2Reliability
If modules execute in serial order, then the implementation is straightforward, but erroneous planned motions are generated that can be dangerous
Solution Approach 1:
Cross-attention mechanisms provide continuous feedback loops between modules, allowing each module to receive and process information from other modules simultaneously. This feedback mechanism enables error detection and correction across modules, improving the reliability and safety of planned motions while distributing the computational complexity across multiple interacting components rather than a single complex sequential process.
3Productivity
If modules execute in serial order, then the processing pipeline is simple, but the time required for real-time control is increased
Solution Approach 1:
The parallel module architecture enables continuous useful action by allowing all modules to process data simultaneously rather than waiting for sequential completion. The cross-attention mechanisms ensure continuous information flow between modules, maximizing productivity by eliminating idle waiting time while maintaining the necessary processing pipeline through coordinated parallel execution.
Data Source
AI summary
One embodiment of a method for controlling a vehicle includes receiving sensor data and information associated with the vehicle, and processing the sensor data and the information via a machine learning model in which a plurality of modules execute in parallel based on one or more cross-attention features to generate a planned motion for the vehicle.