References Khaled Zakaria ZAKKD1804 ENSC105 Ian Brooks 10th February 2020 Reference List Peer Reviewed: Brisken, S., Ruf, F., & Höhne, F. Recent evolution of automotive imaging radar and its...

Lidar and multi-lida
Lidar and multi-lida
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Contents
Introduction    3
Lidar and multi-lidar will help and improve autonomous cars    3
Costs of LIDAR AND Multi-LIDAR    5
LIDAR AND Multi-LIDAR technology detects pedestrians and cars and how safe they are.    5
Ethical and legal approaches of Lidar and multi-lidar    6
Conclusion    7
Reference    8
Executive Summary
This study is an informative paper to the CEO of Tesla to try and persuade him that Lidar and multi-lidar will help and improve autonomous cars. This essay includes information so like how LiDAR and Multi-Lidar technology works and how much it costs and how this technology detects pedestrians and cars and how safe LiDAR and Multi-Lidar are. Finally this essay includes the discussion about the ethical as well as the legal approaches of this technology.
Introduction
Autonomous vehicles are vague signs of an uncertain future and will soon be a thing of the past. Driverless cars will disrupt the automotive business moreover usher in a huge restructuring. In the years to come, you just want to turn around and see them moving in the streets near you. However, an inevitable question arises in our minds: how do self-driving cars work? Of course, many technologies can make a vehicle drive it, however, how do cars change lanes and maintain a secure distance from the other vehicles that hurt the past, and how do they find roadblocks moreover other such obstacles ahead?
Lidar and multi-lidar will help and improve autonomous cars
How this technology (Lidar and Multi-Lidar) works
LIDAR technology is used for ranging and light detection. As its name recommends LIDAR works with factory ground robots or driverless taxis to coastal areas to map and measure deforestation. Autonomous vehicles can use Lidar for obstacle detection as well as avoidance to safely navigate the environment utilizing a rotating laser beam [1]. The cost map and point cloud output of the lidar sensor provides the required data for the robot software’s to determine the presence of probable obstacles in the environment moreover the position of the robot associated with these potential obstacles. Creating a 3D representation by measuring the speed of light and distance covered by it helps determine the location of the vehicle and other su
ounding objects.
Multi-LIDAR: Compared with Lidar systems, multi-Lidar sensors can get better the environmental perceptions of autonomous vehicles. In a multiple Lidar system, the position of the sensor determines the density of the combined point cloud. There are several studies conducted a preliminary study on the optimal LiDAR placement strategy on an off-road autonomous vehicle called the Halo project. Obstacle detection is a significant research area and has many applications in outdoor environments [3]. In particular, the use of obstacle detection for mobile robot tasks (for example avoiding obstacles, pre-collision, mitigating collisions, and stopping) is very high. Moreover, some mobile robots should be capable to execute obstacle detection at high speed in unknown and dynamic environments. For these purposes, laser rangefinders are widely used. Nevertheless, even if the laser scanner is very co
ect, it can only provide a very small scene range. To overcome this restriction of a single laser rangefinder, many studies have focused on multiple laser sensor fusion methods [5]. Particularly, using a 3-D laser scanner to generate point clouds, using INS to accumulate points from a 2-D laser scanner, and stereo vision seems to be a good solution. But, 3-D laser scanners (for example, Velodyne1) are too expensive to use. Cumulative points are sensitive to the frequency and accuracy of INS. To resolve these issues, an effectual two-dimensional laser scanner a
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