Positioning method based on asr

Abstract

The invention relates to an asr-based positioning method, which relates to the technical field of automatic speech recognition, and comprises the following steps: acquiring a journey recording file from a driver end, analyzing the recording file by using ASR SDK, converting audio content into a text, generating an ASR text fragment, and storing the ASR text fragment into a server; the method comprises the following steps: comparing a generated ASR text fragment with a risk word library through an SDK (Software Development Kit), identifying whether a risk word exists in a text, marking a risk label on the text when the risk word is identified, uploading the ASR text with the risk label and a sound recording file to a security service in real time, carrying out abnormal additional recording and data storage, and carrying out LBS (Location Based Service) service. And according to the time range of the ASR text and the driver information, obtaining position information when the risk occurs, and tracking the position of the driver in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic speech recognition, and more specifically, to a positioning method based on ASR. Background Art

[0002] In the modern era of smart travel, online ride-hailing platforms are increasingly becoming the main choice for people to travel. Compared with traditional taxis, online ride-hailing has significant advantages in convenience and flexibility, but it also faces challenges in driver and passenger safety. Therefore, how to effectively improve the level of safety monitoring during the trip has become an important issue to be solved by major online ride-hailing platforms.

[0003] To this end, ASR technology, as a technology that can convert speech into text in real time, has gradually been introduced into online ride-hailing services. Its core purpose is to timely perceive and identify potential dangers or abnormal behaviors. By analyzing the language information extracted from the recording files in real time, the platform can identify keywords related to risk events such as driver-passenger conflicts and traffic accidents, thereby achieving rapid response and intervention.

[0004] At the same time, the combination of LBS location service technology makes this risk monitoring more efficient. LBS technology provides drivers with accurate location storage and query services to ensure that they can grasp the travel dynamics in real time. Summary of the invention

[0005] The present invention aims at the technical problems existing in the prior art and provides an ASR-based positioning method to solve the problems raised in the above background technology.

[0006] The technical solution of the present invention to solve the above technical problem is as follows: a positioning method based on ASR, specifically comprising the following steps:

[0007] Step 101: Obtain a trip recording file from the driver, the trip recording file containing the conversation between the driver and the passenger, use the ASR SDK to analyze the recording file, convert the audio content into text, and generate an ASR text segment;

[0008] Step 102: Compare the generated ASR text segment with the risk word library through the SDK to identify whether there are risk words in the text. When a risk word is identified, a risk tag is added to the text.

[0009] Step 103: Upload the ASR text and recording files with risk labels to the security service in real time for abnormal re-recording and data storage;

[0010] Step 104: Through the LBS service, the location information when the risk occurs is obtained according to the time range of the ASR text and the driver information, and the driver's location is tracked in real time.

[0011] In a preferred embodiment, in step 101, a trip recording file is obtained from the driver, the trip recording file contains the conversation content between the driver and the passenger, and the recording file is analyzed using the ASR SDK to convert the audio content into text and generate an ASR text segment, wherein the ASR SDK is a software development kit for speech recognition. The specific steps are as follows:

[0012] Step A1, recording file acquisition: At the beginning of the trip, the mobile device automatically starts recording the audio of the conversation between the driver and the passenger. The recording process should continue until the end of the trip to ensure that the complete call content is covered. At the end of the trip, the recording stops and the recording file is saved to the cloud database;

[0013] Step A2, speech recognition: Complete the configuration of the ASR SDK software development kit in the project environment, and optimize the audio file, including noise reduction and volume adjustment to improve the recognition effect. Use the ASR SDK to analyze the content of the audio file and convert the voice information into text data. According to the rhythm and content of the conversation, the text data is divided into multiple ASR text segments, and the divided text segments correspond to the semantic structure of the recording.

[0014] In a preferred embodiment, in step 102, the generated ASR text segment is compared with the risk word library through the SDK to identify whether there are risk words in the text. When a risk word is identified, a risk tag is added to the text. The specific steps are as follows:

[0015] Step B1: Establish a risk word library in the cloud database, use the ASR SDK to process the recording file, obtain the generated ASR text, and pre-process the ASR text, including removing redundant spaces, punctuation marks and non-text characters to improve the accuracy of the comparison;

[0016] Step B2: Analyze the words in the ASR text and check whether each word exists in the risk word library. When a word appearing in the ASR text is identified to match any word in the risk word library, it is considered that a risk word is identified and a risk label is added to the text segment.

[0017] In a preferred embodiment, in step 103, the ASR text and recording file with risk tags are uploaded to the security service in real time for abnormal supplementary recording and data storage. The specific steps are as follows:

[0018] Step C1, data transmission: classify and package the ASR text and recording files, upload them uniformly, connect to the security service interface, and send an upload request, wherein the upload request includes the ASR text and recording files with risk tags;

[0019] Step C2, security service processing: The security service receives the uploaded data, performs abnormal supplementary recording and data storage, and further includes the following steps:

[0020] Step C201, data distribution: the security service distributes the received recording files, ASR texts and their corresponding risk tags;

[0021] Step C201, abnormal supplementary recording: When processing the ASR text, the security service identifies and processes the unrecognizable text information and labels, corrects the text information through context analysis, and records the time, file name, and risk label information of this upload;

[0022] Step C201, data storage: storing and synthesizing ASR text segment information, and creating a complete record by associating the ASR text segment with the recording file to facilitate subsequent access and query.

[0023] In a preferred embodiment, in step 104, the location information when the risk occurs is obtained according to the time range of the ASR text and the driver information through the LBS service, and the driver's location is tracked in real time. The specific steps are as follows:

[0024] Step D1, calling the LBS service: extracting the time period when the risk occurs from the ASR text with the risk tag, and obtaining the driver's identity information, including the driver ID and contact information, and sending a request to the LBS service according to the extracted time range to obtain the driver's location information within the time period. The request contains the driver ID and time range to ensure that accurate location information is obtained;

[0025] Step D2: Real-time monitoring of the driver's location is set up through the real-time location tracking function of the LBS service. During the tracking process, when the driver's location is found to be abnormal, including sudden stop or deviation from the scheduled route, an alarm is triggered, further comprising the following steps:

[0026] Step D1, sudden stop: when the driver suddenly stops on the scheduled driving route and remains stationary for more than a 5-minute time threshold, it is considered abnormal;

[0027] Step D2, deviation from the scheduled route: compare the driver's current location with the scheduled driving route in real time, and trigger an alarm when the driver deviates from the normal driving route and does not drive according to the route selected by the rules.

[0028] The beneficial effects of the present invention are as follows: obtaining a trip recording file from the driver side, the trip recording file containing the conversation content between the driver and the passenger, using the ASR SDK to analyze the recording file, converting the audio content into text, and generating an ASR text segment, comparing the generated ASR text segment with the risk word library through the SDK, identifying whether there are risk words in the text, and when a risk word is identified, marking the text with a risk label, uploading the ASR text and recording file with the risk label to the security service in real time, performing abnormal supplementary recording and data storage, and obtaining the location information when the risk occurs according to the time range of the ASR text and the driver information through the LBS service, and tracking the driver's location in real time. An ASR-based positioning method combines ASR with LBS to build a more complete safety monitoring system, which not only improves the timely detection rate of abnormal events, but also can effectively enhance users' trust in the safety and reliability of the platform. In view of the ever-changing travel environment, the integration of ASR and LBS technology can improve the safety of the trip while laying the foundation for the steady development of the online car-hailing industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0031] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0032] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0033] Example 1

[0034] This embodiment provides Figure 1 The ASR-based positioning method specifically comprises the following steps:

[0035] Step 101: Obtain a trip recording file from the driver, the trip recording file containing the conversation between the driver and the passenger, use the ASR SDK to analyze the recording file, convert the audio content into text, and generate an ASR text segment;

[0036] Step 102: Compare the generated ASR text segment with the risk word library through the SDK to identify whether there are risk words in the text. When a risk word is identified, a risk tag is added to the text.

[0037] Step 103: Upload the ASR text and recording files with risk labels to the security service in real time for abnormal re-recording and data storage;

[0038] Step 104: Through the LBS service, the location information when the risk occurs is obtained according to the time range of the ASR text and the driver information, and the driver's location is tracked in real time.

[0039] Preferably, in step 101, a trip recording file is obtained from the driver, the trip recording file contains the conversation content between the driver and the passenger, and the recording file is analyzed using ASR SDK, the audio content is converted into text, and an ASR text segment is generated, wherein the ASR SDK is a software development kit for speech recognition, which saves time and labor costs for manual transcription, making subsequent data processing and analysis easier, and the specific steps are as follows:

[0040] Step A1, recording file acquisition: At the beginning of the trip, the mobile device automatically starts recording the audio of the conversation between the driver and the passenger. The recording process should continue until the end of the trip to ensure that the complete call content is covered. At the end of the trip, the recording stops and the recording file is saved to the cloud database;

[0041] Step A2, speech recognition: Complete the configuration of the ASR SDK software development kit in the project environment, and optimize the audio file, including noise reduction and volume adjustment to improve the recognition effect. Use the ASR SDK to analyze the content of the audio file and convert the voice information into text data. According to the rhythm and content of the conversation, the text data is divided into multiple ASR text segments, and the divided text segments correspond to the semantic structure of the recording.

[0042] Preferably, in step 102, the generated ASR text segment is compared with the risk word library through SDK to identify whether there are risk words in the text. When risk words are identified, the text is labeled with a risk tag, which improves the efficiency and accuracy of risk monitoring, reviews the dialogues involving risks, and helps enterprises to quickly deal with potential problems and reduce losses. The specific steps are as follows:

[0043] Step B1: Establish a risk word library in the cloud database, use the ASR SDK to process the recording file, obtain the generated ASR text, and pre-process the ASR text, including removing redundant spaces, punctuation marks and non-text characters to improve the accuracy of the comparison;

[0044] Step B2: Analyze the words in the ASR text and check whether each word exists in the risk word library. When a word appearing in the ASR text is identified to match any word in the risk word library, it is considered that a risk word is identified and a risk label is added to the text segment.

[0045] Preferably, in step 103, the ASR text and recording files with risk tags are uploaded to the security service in real time, abnormal supplementary recording and data storage are performed, and the risk text and recording are centrally stored in the security service to facilitate data management and retrieval and improve the efficiency of data acquisition. The specific steps are as follows:

[0046] Step C1, data transmission: classify and package the ASR text and recording files, upload them uniformly, connect to the security service interface, and send an upload request, wherein the upload request includes the ASR text and recording files with risk tags;

[0047] Step C2, security service processing: The security service receives the uploaded data, performs abnormal supplementary recording and data storage, and further includes the following steps:

[0048] Step C201, data distribution: the security service distributes the received recording files, ASR texts and their corresponding risk tags;

[0049] Step C201, abnormal supplementary recording: When processing the ASR text, the security service identifies and processes the unrecognizable text information and labels, corrects the text information through context analysis, and records the time, file name, and risk label information of this upload;

[0050] Step C201, data storage: storing and synthesizing ASR text segment information, and creating a complete record by associating the ASR text segment with the recording file to facilitate subsequent access and query.

[0051] Preferably, in step 104, the location information when the risk occurs is obtained according to the time range of the ASR text and the driver information through the LBS service, and the driver's location is tracked in real time, which can improve safety and service quality, reduce empty driving rate and invalid operation, and save costs. The specific steps are as follows:

[0052] Step D1, calling the LBS service: extracting the time period when the risk occurs from the ASR text with the risk tag, and obtaining the driver's identity information, including the driver ID and contact information, and sending a request to the LBS service according to the extracted time range to obtain the driver's location information within the time period. The request contains the driver ID and time range to ensure that accurate location information is obtained;

[0053] Step D2: Real-time monitoring of the driver's location is set up through the real-time location tracking function of the LBS service. During the tracking process, when the driver's location is found to be abnormal, including sudden stop or deviation from the scheduled route, an alarm is triggered, further comprising the following steps:

[0054] Step D1, sudden stop: when the driver suddenly stops on the scheduled driving route and remains stationary for more than a 5-minute time threshold, it is considered abnormal;

[0055] Step D2, deviation from the scheduled route: compare the driver's current location with the scheduled driving route in real time, and trigger an alarm when the driver deviates from the normal driving route and does not drive according to the route selected by the rules.

[0056] Example 2

[0057] This embodiment provides Figure 1 The ASR-based positioning method specifically comprises the following steps:

[0058] Step 101: Obtain a trip recording file from the driver, the trip recording file containing the conversation between the driver and the passenger, use the ASR SDK to analyze the recording file, convert the audio content into text, and generate an ASR text segment;

[0059] Furthermore, in step 101, a trip recording file is obtained from the driver, the trip recording file contains the conversation content between the driver and the passenger, and the recording file is analyzed using the ASR SDK to convert the audio content into text and generate an ASR text segment, wherein the ASR SDK is a software development kit for speech recognition. The specific steps are as follows:

[0060] Step A1, recording file acquisition: At the beginning of the trip, the mobile device automatically starts recording the audio of the conversation between the driver and the passenger. The recording process should continue until the end of the trip to ensure that the complete call content is covered. At the end of the trip, the recording stops and the recording file is saved to the cloud database;

[0061] Step A2, speech recognition: Complete the configuration of the ASR SDK software development kit in the project environment, and optimize the audio file, including noise reduction and volume adjustment to improve the recognition effect. Use the ASR SDK to analyze the content of the audio file and convert the voice information into text data. According to the rhythm and content of the conversation, the text data is divided into multiple ASR text segments, and the divided text segments correspond to the semantic structure of the recording.

[0062] Step 102: Compare the generated ASR text segment with the risk word library through the SDK to identify whether there are risk words in the text. When a risk word is identified, a risk tag is added to the text.

[0063] Furthermore, in step 102, the generated ASR text segment is compared with the risk word library through the SDK to identify whether there are risk words in the text. When a risk word is identified, a risk tag is added to the text. The specific steps are as follows:

[0064] Step B1: Establish a risk word library in the cloud database, use the ASR SDK to process the recording file, obtain the generated ASR text, and pre-process the ASR text, including removing redundant spaces, punctuation marks and non-text characters to improve the accuracy of the comparison;

[0065] Step B2: Analyze the words in the ASR text and check whether each word exists in the risk word library. When a word appearing in the ASR text is identified to match any word in the risk word library, it is considered that a risk word is identified and a risk label is added to the text segment.

[0066] Step 103: Upload the ASR text and recording files with risk labels to the security service in real time for abnormal re-recording and data storage;

[0067] Furthermore, in step 103, the ASR text and recording files with risk tags are uploaded to the security service in real time for abnormal re-recording and data storage. The specific steps are as follows:

[0068] Step C1, data transmission: classify and package the ASR text and recording files, upload them uniformly, connect to the security service interface, and send an upload request, wherein the upload request includes the ASR text and recording files with risk tags;

[0069] Step C2, security service processing: The security service receives the uploaded data, performs abnormal supplementary recording and data storage, and further includes the following steps:

[0070] Step C201, data distribution: the security service distributes the received recording files, ASR texts and their corresponding risk tags;

[0071] Step C201, abnormal supplementary recording: When processing the ASR text, the security service identifies and processes the unrecognizable text information and labels, corrects the text information through context analysis, and records the time, file name, and risk label information of this upload;

[0072] Step C201, data storage: storing and synthesizing ASR text segment information, and creating a complete record by associating the ASR text segment with the recording file to facilitate subsequent access and query.

[0073] Step 104: Using the LBS service, obtain the location information when the risk occurs according to the time range of the ASR text and the driver information, and track the driver's location in real time;

[0074] Furthermore, in step 104, the location information when the risk occurs is obtained according to the time range of the ASR text and the driver information through the LBS service, and the driver's location is tracked in real time. The specific steps are as follows:

[0075] Step D1, calling the LBS service: extracting the time period when the risk occurs from the ASR text with the risk tag, and obtaining the driver's identity information, including the driver ID and contact information, and sending a request to the LBS service according to the extracted time range to obtain the driver's location information within the time period. The request contains the driver ID and time range to ensure that accurate location information is obtained;

[0076] Step D2: Real-time monitoring of the driver's location is set up through the real-time location tracking function of the LBS service. During the tracking process, when the driver's location is found to be abnormal, including sudden stop or deviation from the scheduled route, an alarm is triggered, further comprising the following steps:

[0077] Step D1, sudden stop: when the driver suddenly stops on the scheduled driving route and remains stationary for more than a 5-minute time threshold, it is considered abnormal;

[0078] Step D2, deviation from the scheduled route: compare the driver's current location with the scheduled driving route in real time, and trigger an alarm when the driver deviates from the normal driving route and does not drive according to the route selected by the rules.

[0079] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0080] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0084] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0085] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A positioning method based on ASR, characterized in that: The specific steps include: Step 101: Obtain a trip recording file from the driver, the trip recording file containing the conversation between the driver and the passenger, use the ASR SDK to analyze the recording file, convert the audio content into text, and generate an ASR text segment; Step 102: Compare the generated ASR text segment with the risk word library through the SDK to identify whether there are risk words in the text. When a risk word is identified, a risk tag is added to the text. Step 103: Upload the ASR text and recording files with risk labels to the security service in real time for abnormal re-recording and data storage; Step 104: Through the LBS service, the location information when the risk occurs is obtained according to the time range of the ASR text and the driver information, and the driver's location is tracked in real time.

2. The ASR-based positioning method according to claim 1, characterized in that: In step 101, a trip recording file is obtained from the driver, and the trip recording file contains the conversation content between the driver and the passenger. The recording file is analyzed using the ASR SDK, the audio content is converted into text, and an ASR text segment is generated. The specific steps are as follows: Step A1, recording file acquisition: At the beginning of the trip, the mobile device automatically starts recording the audio of the conversation between the driver and the passenger. The recording process should continue until the end of the trip. At the end of the trip, the recording is stopped and the recording file is saved to the cloud database; Step A2, speech recognition: Complete the configuration of ASR SDK in the project environment, and optimize the audio file, including noise reduction and volume adjustment, to improve the recognition effect. Use ASR SDK to analyze the content of the audio file and convert the voice information into text data. According to the rhythm and content of the conversation, the text data is divided into multiple ASR text segments, and the divided text segments correspond to the semantic structure of the recording.

3. The ASR-based positioning method according to claim 1, characterized in that: In step 102, the generated ASR text segment is compared with the risk word library through the SDK to identify whether there are risk words in the text. When a risk word is identified, a risk tag is added to the text. The specific steps are as follows: Step B1: Establish a risk word library in the cloud database, use the ASR SDK to process the recording file, obtain the generated ASR text, and pre-process the ASR text, including removing redundant spaces, punctuation marks and non-text characters; Step B2: Analyze the words in the ASR text and check whether each word exists in the risk word library. When a word appearing in the ASR text is identified to match any word in the risk word library, it is considered that a risk word is identified and a risk label is added to the text segment.

4. The ASR-based positioning method according to claim 1, characterized in that: In step 103, the ASR text and recording files with risk tags are uploaded to the security service in real time for abnormal supplementary recording and data storage. The specific steps are as follows: Step C1, data transmission: classify and package the ASR text and recording files, upload them uniformly, connect to the security service interface, and send an upload request, wherein the upload request includes the ASR text and recording files with risk tags; Step C2, security service processing: The security service receives the uploaded data, performs abnormal supplementary recording and data storage.

5. The ASR-based positioning method according to claim 4, characterized in that: In the step C2 security service processing, the security service receives the uploaded data, performs abnormal supplementary recording and data storage, and further includes the following steps: Step C201, data distribution: the security service distributes the received recording files, ASR texts and their corresponding risk tags; Step C201, abnormal supplementary recording: When processing the ASR text, the security service identifies and processes the unrecognizable text information and labels, corrects the text information through context analysis, and records the time, file name, and risk label information of this upload; Step C201, data storage: storing and synthesizing ASR text segment information, and creating a complete record by associating the ASR text segment with the recording file to facilitate subsequent access and query.

6. The ASR-based positioning method according to claim 1, characterized in that: In step 104, the location information when the risk occurs is obtained according to the time range of the ASR text and the driver information through the LBS service, and the driver's location is tracked in real time. The specific steps are as follows: Step D1, calling the LBS service: extracting the time period when the risk occurs from the ASR text with the risk tag, and obtaining the driver's identity information, including the driver ID and contact information, and sending a request to the LBS service according to the extracted time range to obtain the driver's location information within the time period. The request contains the driver ID and time range to ensure that accurate location information is obtained; Step D2: Set up real-time monitoring of the driver's location through the real-time location tracking function of the LBS service. During the tracking process, if the driver's location is abnormal, including sudden stop or deviation from the scheduled route, an alarm is triggered.

7. The ASR-based positioning method according to claim 1, characterized in that: In the step D2, when it is found that the driver's position is abnormal, including sudden stop, deviation from the scheduled route, an alarm is triggered, and the following steps are further included: Step D1, sudden stop: when the driver suddenly stops on the scheduled driving route and remains stationary for more than a 5-minute time threshold, it is considered abnormal; Step D2, deviation from the scheduled route: compare the driver's current location with the scheduled driving route in real time, and trigger an alarm when the driver deviates from the normal driving route and does not drive according to the route selected by the rules.

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