PATENT CLAIM ANALYSIS

Application Number: 16308353
Application Type: Utility
Filing Date: 2018-12
Publication Date: 2019-08
Patent Classification: ["701", "027000"]

Abstract:
A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model (S 110 ); partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result (S 120 ); and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result (S 130 ). The present disclosure effectively solves the problem that a model trained by means of machine learning cannot be locally adjusted and easily modifies the decision of a certain partition without affecting the decision results of other partitions at all. The intuitive nature of a partition decision table can effectively help to find and solve problems in the machine learning process. The partition decision table can speed up the decision process.

Claim (Index 23):
A calculating apparatus for speed planning of automatic driving of a vehicle, comprising a storage component and a processor, wherein the storage component stores a computer executable instruction set that, when executed by the processor, cause the processor to perform:\n a machine learning step, comprising: performing machine learning using a set of training samples to obtain a machine learning model, wherein each training sample is represented by a multi-dimensional feature vector forming an input space and a decision result forming an output space, wherein each dimension of the multi-dimensional feature vector is a variable describing a state of the vehicle at a particular moment, wherein the variable is related to speed planning, and wherein the decision result indicates at least one of an expected speed at a next moment or a control parameter value related to speed control; a partition decision table obtaining step, comprising: partitioning the input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table that maps each of the partitions to its corresponding decision result; a real-time decision-making step, comprising: obtaining each dimensional feature vector of the vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain a corresponding decision result; and a real-time control step, comprising: issuing a control command to the vehicle based on the obtained decision result to control a speed of the vehicle.

Metadata:
- Claim Count in Document: 42.0
- Percentile: 98.0
- Lexical Diversity: 2.17708
- Patent Class: 701.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16134134', '13990146', '14680892', '16016691', '15484282']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3692786905862142
- 35 USC 102 Novelty (BERT): 0.4894667253949227
- Combined Prediction Score: 0.3812974940670851
- Mean Citation Score: 174.39188399999995
- Max Citation Score: 186.18304
- Similarity Product: 125.7044049190903

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 0

Dataset: test