Invention Grant
- Patent Title: Probabilistic neural network for predicting hidden context of traffic entities for autonomous vehicles
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Application No.: US16783845Application Date: 2020-02-06
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Publication No.: US11467579B2Publication Date: 2022-10-11
- Inventor: Jacob Reinier Maat , Samuel English Anthony
- Applicant: Perceptive Automata, Inc.
- Applicant Address: US MA Boston
- Assignee: Perceptive Automata, Inc.
- Current Assignee: Perceptive Automata, Inc.
- Current Assignee Address: US MA Boston
- Agency: Fenwick & West LLP
- Main IPC: G05D1/00
- IPC: G05D1/00 ; G06N7/00 ; G06N3/08 ; G05D1/02 ; B60W60/00

Abstract:
An autonomous vehicle uses probabilistic neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The probabilistic neural network is configured to receive an image of traffic as input and generate output representing hidden context for a traffic entity displayed in the image. The system executes the probabilistic neural network to generate output representing hidden context for traffic entities encountered while navigating through traffic. The system determines a measure of uncertainty for the output values. The autonomous vehicle uses the measure of uncertainty generated by the probabilistic neural network during navigation.
Public/Granted literature
- US20200249677A1 PROBABILISTIC NEURAL NETWORK FOR PREDICTING HIDDEN CONTEXT OF TRAFFIC ENTITIES FOR AUTONOMOUS VEHICLES Public/Granted day:2020-08-06
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