Occupancy Prediction Neural Networks Without Agent Tracking
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Solution Overview
Problem
Conventional occupancy prediction systems for autonomous vehicles face challenges in accurately predicting future occupancy of target regions by agents beyond the effective range of sensors, leading to inaccurate predictions due to the need for agent localization and tracking, which is error-prone and computationally intensive.
Innovation Solution
An occupancy prediction neural network processes sensor data, including camera and lidar data, without explicit localization and tracking of agents, directly predicting the likelihood of region occupancy in future time intervals, using a convolutional neural network architecture that consumes fewer computational resources and can be trained with automatically generated labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional occupancy prediction systems perform explicit agent localization and tracking, then they can identify agent positions and movements, but the computational complexity increases and prediction accuracy decreases for agents beyond sensor range
Solution Approach 1:
The patent extracts and removes the explicit agent localization and tracking components from the occupancy prediction system. Instead of localizing and tracking agents, the system directly predicts occupancy of target regions using sensor data and neural networks, eliminating the intermediate steps that cause computational complexity and error propagation.
Solution Approach 2:
The patent inverts the conventional approach by not predicting agent positions and movements first, but directly predicting whether target regions will be occupied. This inversion bypasses the need for agent localization and tracking, reducing computational complexity while improving accuracy for distant agents.
2Reliability
If conventional systems track agents beyond sensor effective range, then they attempt to predict future positions, but prediction accuracy deteriorates due to error propagation
Solution Approach 1:
The patent removes the agent tracking component that causes error propagation for distant agents. By directly predicting region occupancy without tracking agent positions, the system eliminates the accumulation of localization and tracking errors that deteriorate prediction reliability.
3Productivity
If the system processes sensor data through multiple localization and tracking stages, then it follows conventional pipelines, but processing time and computational resources increase
Solution Approach 1:
The patent extracts and eliminates the intermediate localization and tracking processing stages, directly transforming sensor data into occupancy predictions through neural networks. This streamlining removes unnecessary computational steps, improving processing efficiency and reducing computational time.
Solution Approach 2:
The system performs preliminary processing of sensor data into feature representations that directly encode occupancy information, eliminating the need for subsequent localization and tracking operations. This preliminary transformation accelerates the overall processing pipeline.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a future occupancy prediction for a region of an environment. In one aspect, a method comprises: receiving sensor data generated by a sensor system of a vehicle that characterizes an environment in a vicinity of the vehicle as of a current time point, wherein the sensor data comprises a plurality of sensor samples characterizing the environment that were each captured at different time points; processing a network input comprising the sensor data using a neural network to generate an occupancy prediction output for a region of the environment, wherein: the occupancy prediction output characterizes, for one or more future intervals of time after the current time point, a respective likelihood that the region of the environment will be occupied by an agent in the environment during the future interval of time.