Adaptive Vehicle Perception Zones for Sensor Load Reduction
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Solution Overview
Problem
Processing vehicle sensor data can be computationally intensive, making it inefficient for time-sensitive and mobile applications.
Innovation Solution
Adaptive processing of incoming sensor data using a perception model, where a vehicle computer determines a perception zone of interest based on vehicle parameters and planning data, and selectively processes sensor data relevant to this zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all sensor data is processed, then perception accuracy is improved, but computational load increases
Solution Approach 1:
The patent segments sensor data processing by dividing the environment into multiple zones of interest (e.g., front, rear, left, right zones) and selectively processing data from different zones based on vehicle operating conditions. This segmentation allows the system to process only relevant portions of sensor data rather than all data uniformly, reducing computational load while maintaining perception accuracy for critical areas.
Solution Approach 2:
The patent applies local quality by adjusting processing intensity and attention levels for different spatial zones based on their relevance to current vehicle operations. High-priority zones (e.g., front zone when vehicle is moving forward) receive intensive processing with higher computational resources, while low-priority zones receive reduced processing. This creates non-uniform processing quality distributed across different locations, optimizing the balance between accuracy and computational load.
2Productivity
If sensor data processing is reduced, then computational efficiency is improved, but perception reliability deteriorates
Solution Approach 1:
The patent implements dynamic adjustment of processing intensity based on real-time vehicle operating conditions, including speed, acceleration, steering angle, and detected objects. When the vehicle is in stable conditions (low speed, straight driving), processing intensity is reduced for non-critical zones. When dynamic conditions arise (high speed, turning, object detected), processing intensity automatically increases for relevant zones. This dynamic adaptation maintains perception reliability during critical moments while improving computational efficiency during stable periods.
Solution Approach 2:
The system incorporates feedback mechanisms where perception results and detection outcomes influence subsequent processing decisions. If objects are detected in a zone or if perception uncertainty is high, the system increases processing intensity for that zone to ensure reliable detection. Conversely, if areas are confirmed clear and stable, processing is reduced. This feedback loop ensures that perception reliability is maintained when needed while allowing computational efficiency improvements when conditions permit.
Data Source
AI summary
A system comprises a computer including a processor and a memory. The memory storing instructions executable by the processor to cause the processor to determine a perception zone of interest, via a trained perception model, based on at least one of a vehicle parameter or planning data, wherein the vehicle parameter comprises at least one of a vehicle direction or a vehicle speed and the planning data comprises a route and trajectory to be traversed by the vehicle; determine a vehicle route and trajectory based on a subset of sensor data, wherein the subset of sensor data corresponds to the perception zone of interest; and operate the vehicle to traverse the vehicle route and trajectory.