The architecture design of the system is shown in the Figure 1. The manager plans the route of the vehicle to be driven in advance through the page, and the route planning can be completed through the commonly used Baidu API, or it can be completed in a custom way.

In Figure 1, the external module interface is provided to store the route defined by the manager and query the final route deviation warning results; The planned routes stored in the relational database are synchronized to the dimension table for real-time big data analysis in HBase by data synchronization.

The real-time analysis and processing module in Figure 1, specifically storm stream processing analysis code will read real-time location data reported by the vehicle from the message queue in real time, and combined with the route rules in the HBase dimension table, determine whether the vehicle currently deviates from the planned route, and send the corresponding warning result to the message queue. The message queue here is used for decoupling the result data. To prevent too much and too fast result data, affecting downstream applications.

Route planning can be achieved through the manager’s custom and Baidu API interface two ways.

In the customized mode of the manager, users operate on the map, record the characteristic inflection points of the planned path from A to M (such as A, B, C, D, etc.), and then call the system interface to complete the storage of the planned route information, as shown in Figure 2.

All the fix points in the planned path can be obtained through Baidu API interface, as shown in Figure 3, but there are too many track points of the entire route. In order to reduce the storage of the entire data and the consumption of computing resources, route extraction is required. For N path points on the same line, only the path points of the first and last two endpoints need to be obtained. Then each characteristic inflection point is obtained to complete the route planning Settings.

The monitoring cycle of the whole route planning is the period from the driver’s click to execute the start task to the driver’s click to end the task, and the rest period is not required for monitoring.

Firstly, route planning was carried out for the experimental vehicle. A total of 12 route sections from A to L were planned, and the road width was set at 5 m, as shown in Figure 9.

The latitude and longitude information of each segment is shown in Table 1, and the latitude and longitude coordinates are reserved for 4 decimal places.

The actual driving path of the vehicle is shown in Figure 10.

The vehicle trajectory is analyzed in real time through the whole system, and the vehicle deviation alarm results stored in the relational database are shown in Table 2.

The starting and ending positions of the first warning are shown in Figure 11.

The reason for this alarm is that the difference between the starting point of path planning and the starting point of vehicle start-up is more than 5 m of the set road width. When the vehicle travels near the starting point of the path planning, the warning ends. The starting point of path planning is shown in Figure 12.

The starting and ending positions of the second alert generation are shown in Figure 13.

The starting position of the alarm is located at the starting point of the planned route F, and the end position of the alarm is located at the starting point of the planned route L. Because the vehicle fails to follow the planned route E → F → G → H → I → J → K → L, but travels from route E to route L (E → L), this alarm is consistent with the actual situation.

In view of this experiment, we analyze and compare the fence discrimination algorithm and the segmentation analysis algorithm from two aspects of resource storage consumption and execution time.

In terms of resource storage consumption, the longitude and latitude coordinates of feature points in the planned path are stored using Float type, Float occupies 4 bytes, and a group of latitude and longitude occupies 8 bytes. The segmentation analysis algorithm only needs to store each feature point, and the fence discrimination algorithm needs to convert every 2 feature points into 4 fence coordinates for storage.

In terms of execution time, for each route segment in the planned path, the segmented analysis algorithm needs to perform circular region judgment at least once, and secondary circular region judgment and distance judgment at most. Assuming that all judgment times are m, the analysis execution time of each route segment is m ≤ t ≤ 3m. The fence discrimination algorithm makes N fence judgments for each route segment, that is,

t = N ∗ m ,

where N represents the number of route segments.

The storage and execution time of the algorithm in this experiment are shown in Table 3.

Through the theoretical analysis and the comparison of the actual results, it can be seen that the segmentation analysis algorithm is better than the fence discrimination algorithm. At the same time, with the increase of the planned path segment, the difference in storage occupancy and execution time will become more obvious.