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Auxiliary driving method and device, electronic equipment and storage medium

A technology of assisted driving and driving intention, applied in the fields of assisted driving methods, electronic equipment and storage media, and devices, can solve the problems of driving behaviors that cannot be assisted by the driver and cannot be predicted by the driver, so as to improve the depth of training and enhance stability , to achieve the effect of comprehensive consideration

Pending Publication Date: 2022-01-28
JINGDONG KUNPENG (JIANGSU) TECH CO LTD
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AI Technical Summary

Problems solved by technology

[0004] In the process of simulating the driver's cognition and decision-making in the algorithm framework, although the driver's driving behavior can be well explained and analyzed, it is impossible to predict the driver's next driving behavior, resulting in the existence of the obtained driving behavior. The problem that the behavior cannot assist the driver

Method used

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  • Auxiliary driving method and device, electronic equipment and storage medium
  • Auxiliary driving method and device, electronic equipment and storage medium
  • Auxiliary driving method and device, electronic equipment and storage medium

Examples

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Embodiment 1

[0032] figure 1 It is a schematic flow chart of a driving assistance method provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation of modeling the driving intention prediction task, and the method can be executed by a driving assistance device, which can and / or in the form of hardware, where the hardware may be an electronic device, such as a mobile terminal, a PC, or a server.

[0033] Such as figure 1 As shown, the method specifically includes the following steps:

[0034] S110. Obtain an external environment video of the environment to which the target vehicle belongs, and a target user video corresponding to the target vehicle.

[0035] Wherein, the environment to which the vehicle belongs may be an area divided by the system based on the preset length or range with the current position of the vehicle as the center, for example, a circular area determined with the current position of the vehicle as the center and a radius of 10 ...

Embodiment 2

[0056] figure 2 It is a schematic flow chart of a driving assistance method provided by Embodiment 2 of the present invention. On the basis of the foregoing embodiments, the features of multiple sources inside and outside the vehicle can be obtained based on visual sensors and image acquisition devices; LSTM can be used to make full use of context information to Perform time series prediction tasks. At the same time, combining LSTM and CRF can fully consider the constraint relationship between each moment in the process of driving intention prediction; the introduction of hole convolution network reduces the memory consumption during model training, and realizes Parallel optimization of the model, and further, adding a time delay in the process of processing the features outside the vehicle makes the model more rational and interpretable at the cognitive level, thus building a cognitive prior and data two-way drive Driving Intention Prediction Network. For its specific imple...

Embodiment 3

[0087] Figure 6 It is a structural block diagram of a driving assistance device provided in Embodiment 3 of the present invention, which can execute the driving assistance method provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. Such as Figure 6 As shown, the device specifically includes: a video acquisition module 310 , a feature sequence determination module 320 to be processed, a feature sequence determination module 330 to be fused, and a target feature sequence determination module 340 .

[0088] The video acquisition module 310 is configured to acquire the external environment video of the environment to which the target vehicle belongs, and the video of the target user corresponding to the target vehicle.

[0089]The to-be-processed feature sequence determination module 320 is configured to process the external environment video and the target user video based on the pre-buil...

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Abstract

The embodiment of the invention discloses an auxiliary driving method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining an external environment video of an environment to which a target vehicle belongs and a target user video corresponding to the target vehicle; processing the acquired video based on a pre-constructed bidirectional neural network to obtain a to-be-processed external environment feature sequence and a to-be-processed user feature sequence; respectively inputting the to-be-processed feature sequence into a pre-constructed time convolutional network to obtain a to-be-fused external environment feature sequence and a to-be-fused user feature sequence; and determining a target feature sequence corresponding to the target vehicle based on the to-be-fused feature sequence, so as to determine a driving intention of a target user corresponding to the target vehicle based on the target feature sequence. According to the technical scheme provided by the embodiment of the invention, comprehensive consideration of inside and outside feature information of the vehicle is realized in the driving intention prediction process, and meanwhile, the stability of a driving intention prediction framework is enhanced.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of automobiles, and in particular to a driving assistance method, device, electronic equipment, and storage medium. Background technique [0002] In the driving intention prediction task, the commonly used method is a data-driven algorithm, which does not take into account the intrinsic correlation between each feature, resulting in the inability to accurately predict the driver's driving intention. Based on this, the driver's cognition and decision-making process can be simulated in the algorithm framework, so that the algorithm can perform closer to the driver's decision-making performance in the prediction process. [0003] When the inventor implemented the technical solution based on the above method, he found the following problems: [0004] In the process of simulating the driver's cognition and decision-making in the algorithm framework, although the driver's driving behavior ca...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/56G06V20/59G06V10/44G06V10/764G06V10/80G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/048G06N3/044G06F18/241G06F18/25
Inventor 徐鑫
Owner JINGDONG KUNPENG (JIANGSU) TECH CO LTD
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