Intelligent network connection automobile public road dangerous scene extraction method and device, dangerous scene construction method and device and computing equipment
An extraction method and intelligent network technology, applied in the field of driving scene construction, can solve the difficulties of driving assistance functions, development of automatic driving functions, unmanned driving, user property and personal safety injuries, dangerous scene recognition capabilities, planning capabilities, and decision-making capabilities , Insufficient executive ability and other issues, to reduce misjudgment
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Embodiment approach 1
[0091] Embodiment 1. See figure 1 Illustrating this embodiment, the method for extracting a dangerous scene on a public road for an intelligent networked vehicle described in this embodiment includes the following steps:
[0092] Data collection step: collect the video information obtained by the vehicle loaded with the data collection device during the driving process, and the driving information and status information of the vehicle as scene data to be processed;
[0093] The data co-frequency processing step is to adjust the data of different frequencies in the scene data to be processed to the same frequency;
[0094] Dangerous scene trigger condition judgment step: Read the information in the scene data in chronological order to determine whether the dangerous scene trigger conditions are met. If so, start the dangerous scene extraction step; if not, close the dangerous scene extraction step and start the dangerous scene. Scene storage steps;
[0095] Dangerous scene ex...
Embodiment approach 2
[0107] Embodiment 2. This embodiment is a further limitation of the method for extracting dangerous scenes on public roads of intelligent networked vehicles described in Embodiment 1. In this embodiment, the data collection device includes a video collection device, a target object recognition equipment, CAN data acquisition equipment and GPS positioning equipment, including:
[0108] The video collection device is used to collect video information in front of the vehicle;
[0109] The target object identification device is used to identify the target object type in the video information collected by the video capture device, and to mark the target object;
[0110]The CAN data acquisition device is used to collect and store the marked video information of the target; it is also used to collect the information obtained by the vehicle lidar system; it is also used to collect the information obtained by the vehicle millimeter wave system; it is also used to collect the vehicle T...
Embodiment approach 3
[0122] Embodiment 3. This embodiment is a further limitation of the method for extracting a dangerous scene on a public road of an intelligent networked vehicle described in Embodiment 1 or 2. The triggering condition of the dangerous scene described in this embodiment is based on the driving state of the vehicle. Different conditions are set, specifically:
[0123] The dangerous scene trigger condition refers to meeting any one of the following conditions:
[0124] Condition 1: c≤d 0 And v≥20km / h, where D is the distance between the vehicle and the preceding vehicle; where d 0 In order to judge whether it is necessary to detect the critical distance of the target vehicle, v is the speed of the test vehicle, and c is the distance between the test vehicle and the target vehicle in front;
[0125] Condition 2: d 0 1 and v≥36km / h; d 1 Indicates the critical distance for judging whether it is necessary to measure distance or relative speed;
[0126] Condition 3: d 1 max time...
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