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Essay for the dissertation

Essay for the dissertation 5LVnA

Anomaly detection of vehicles requires accurate vehicles’ positions for monitoring the movement of others and predicting other vehicles behaviors. To propose the exact location of surrounding vehicles, this paper explores the ways to accurately estimate the lateral distance between mid-low point on the rear of the vehicle and adjacent lane by detecting lane markers and objects with orientation information. Both are strongly dependent on advantages of Camera and LiDAR sensors and rely on Deep Neural Network. To estimate vertical distance of other vehicles about lanes, we detect the lanes and objects with image-based and point cloud-based deep learning model respectively. Additionally, we focus on projection process which put 3D point cloud on image domain to merge the detection results from different dimensions. By putting matching process on projection step, it is able to find 3D lanes corresponding to 2D lane detection results, so getting the lateral distance between target points on vehicles and lanes is possible with derivative relation. In order to present the estimation performance of our method, this paper considers errors in the rear center point of the 3D objection box and the center point on the actual 3D map, and considers errors occurring in mapping lanes, detected in 2D, to 3D.

Anomaly detection of You use this word too often, consider replacing it withwagonvancarbustruckautomobile
vehicles
 requires accurate You use this word too often, consider replacing it withwagonvancarbustruckautomobile
vehicles’
 positions for monitoring the movement of others and predicting other You use this word too often, consider replacing it withwagonvancarbustruckautomobile
vehicles
 behaviors. To propose the exact location of surrounding You use this word too often, consider replacing it withwagonvancarbustruckautomobile
vehicles
, this paper explores the ways to accurately
 estimate the lateral distance between mid-low You use this word too often, consider replacing it withstopmark
point
 on the rear of the You use this word too often, consider replacing it withwagonvancarbustruckautomobile
vehicle
 and adjacent You use this word too often, consider replacing it withroadwayboulevardthoroughfareroutestreetartery
lane
 by detecting You use this word too often, consider replacing it withroadwayboulevardthoroughfareroutestreetartery
lane
 markers and objects with orientation information. Both are strongly
 dependent on advantages of Camera and Possible spelling mistake found.LIDARLiarLi DARLiD AR
LiDAR
 sensors and rely on Deep Neural Network.

To estimate vertical distance of other You use this word too often, consider replacing it withwagonvancarbustruckautomobile
vehicles
 about You use this word too often, consider replacing it withroadwayboulevardthoroughfareroutestreetartery
lanes
, we detect the You use this word too often, consider replacing it withroadwayboulevardthoroughfareroutestreetartery
lanes
 and objects with image-based and You use this word too often, consider replacing it withstopmark
point
 cloud-based deep learning model respectively
. Additionally
, we focus on projection process which put 3D You use this word too often, consider replacing it withstopmark
point
 cloud on image domain to merge the detection results from This phrase/word is too simple and can be replaced with:various
different
 dimensions. By putting matching process on projection step, it is able to find 3D You use this word too often, consider replacing it withroadwayboulevardthoroughfareroutestreetartery
lanes
 corresponding to 2D You use this word too often, consider replacing it withroadwayboulevardthoroughfareroutestreetartery
lane
 detection results, This phrase/word is too simple and can be replaced with:consequently
so
 getting the lateral distance between target You use this word too often, consider replacing it withstopmark
points
 on You use this word too often, consider replacing it withwagonvancarbustruckautomobile
vehicles
 and You use this word too often, consider replacing it withroadwayboulevardthoroughfareroutestreetartery
lanes
 is possible with derivative relation.

In order to present the estimation performance of our method, this paper considers errors in the rear center You use this word too often, consider replacing it withstopmark
point
 of the 3D objection box and the center You use this word too often, consider replacing it withstopmark
point
 on the actual 3D map, and considers errors occurring in mapping You use this word too often, consider replacing it withroadwayboulevardthoroughfareroutestreetartery
lanes
, detected in 2D, to 3D. 