Introduction

Autonomous vehicles have played an important role in technological development and advancement in the future of cars. Increased production of self-employed however comes with several considerations including the environmental impacts and other ethical considerations (Levinson et al., 2011). Research on autonomous systems has also led to the improvement with dramatic advancement and other available computers at a reduced cost that requires computing technologies.   The most commonly used computing technologies in autonomous vehicles are lidar, radar, and vision. The lidar technology has numerous advantages including efficiency, dependency and a high level of accuracy.  This makes a perfect sense for the market of the self-driving vehicles. Many developers of autonomous cars use LiDAR for vehicles except Tesla. Despite this, several disadvantages come with lidar sensors. One of them is that they are not easy to acquire and they are quite expensive.  Because of these factors, there is a need to have better alternatives. The alternatives include; radar, vision, and AD, all of which have the advantage over lidar.

Requirements, needs, and specification associated with sensor technology

There are several requirements of a better sensor technology that is used in an autonomous car.The anticipation surrounding autonomous vehicles calls for better conditions and the need for improvements in road safety to eliminate human errors. Most of the accidents that took place on the road come from the errors done by the humans and the burden that is placed on the human drivers (Amzajerdian et al., 2018). Self-driven vehicles should result in better productivity and enable people the time to rest as well as combining the different foreseen benefits with the ongoing anticipations. The past three decades of leadership to a steady increase in efforts to develop technology for self-driving vehicles as well as advances in sensing and computer technologies. Major research works and the need for efficiency have also fuelled this growth lowering their prices of self-driving computer technology. The perceived societal benefits have continuously grown as the population around the globe also glows. The increase in population has also lead to increasing vehicle ownership with the number of vehicles in the world are currently estimated should be 1.7 billion as the population heads towards 8 billion.

Possible autonomous control and processing options

There are several possible autonomous controls and processing options for the unmanned systems that are based on the operational environment and availability of new technology. These controls are witnessed in some of the business towns around. Autonomous cars are those that are driven by 21st-century drivers. Autonomous cars operate through the use of LIDAR, vision, AD or Radar (Filisetti et al., 2018). These are the readily available options to the cars. Lidar is a piece of hardware that unlocks self-driving cars for people. Lidar operates just like a reader but emits infrared laser instead of sending radio waves. These leases are invisible to the human eye, and they measure the distance that the car will take to heat any nearby object. This process is done more than five million times within a second and compares the result in a way that it points to a cloud that works in a real map world giving the details of the sport that has been identified. Once identified the object, the computer of the card and then predict the behavior at the object and how the car should be driven. Self-driving cars use other sensors that can be noted like radars and cameras. However, all the senses cannot match the laser vision. The technology has proven to be reliable and also offer digital resolution by giving a detailed translation of 2d images that requires machine learning power and puts it into a 3-D understanding. On the contrary, Lidar offers data that is in the form of direct measurement and one that is computer friendly.

The appropriateness of considered elements and the components that support the intended use

Various ideas have to be considered for the intended use. Several weaknesses of Lidar that have led to many companies competing for the alternatives to this technology. The main weakness is that Lidar is lack of toughness for the road especially the rough roads. Lidarsensing techniques have also been around for several decades especially those that have been used to map the surrounding.This technology offers a different kind of vision. Primitive laser scanners have also been pushed for the development of autonomous vehicles. Apart from the lack of toughness that is associated with lidar, there are other limitations that it has (Wolcott, and Eustice, 2017). The technology is quite expensive as the cheapest sensor that can be found in the market is $4000.  For a car to successfully incorporate LIDAR into full operation, it has to have several sensors that would cover on its surrounding.The other limitation is that lidar works the car too much and in a hard way. Technology and other devices that are integrated with the cars have to be robust and why important to leave reliable as they have to withstand all the potholes and temperature variations. The device also has to work for several years continuously.  Deploying other technologies such as AVsand vision have made it simple for the people. The other downside is that the lidar sensors have to entirely rely on weather conditions (Messikommer, Schaefer, Dubé, and Pfeiffer, 2017). The technology does not give an inaccurate image in case there are challenging conditions such as a form or a storm hits the road. This means that the technology must be paired with another secondary sensor. The problem is that they are quite expensive that the user has to incorporate the secondary sensor. Lidar cannot effectively detect the speed of the other vehicles. Spinning lidar that transmits lasers usually shoot them to different directions requiring different moving parts. This leads to an increase in maintenance and installation cost. The 3-D images that are generated allow the computer to predict the actions of the road users can’t help in deciding for the next movement.

Major sensor alternatives

There are alternatives such as radar, AV and vision. These are technologies that are based on the environmental safety and availability of new technologies. The environment has a massive role to play in developing the controls for autonomous cars. Depending on the nature of the road, a specific type of technology have to be used to ensure the longevity of the vehicle. These sensors act as the eye to these autonomous cars (Mollison, 2017). Lidars and radar systems have been competing for the top spot about the most effective system that can be used in autonomous cars. The competition between these two senses has led to an intense rivalry within the automotive industry. Many companies such as ford and general motors usually use lidar. Alternatives are also available for the cars including Luminar that has presented a massive competition for these two senses.

Autonomous controls

These are various technological operations for these lasers. These technologies used light to detect the range of an object and the surrounding of the car. LiDARuses the near-infrared light that scans objects to provide a three-dimensional map of the surroundings of the car. By transmitting with a laser beam that bounces of the object, the image of the object is returned to the sensor.There have to be several considerations that can provide the most accurate technology that can be used in driving. The technology has to give a 360-degree view of the surrounding. This has to be combined with the identification of the objects that can see through the road and other vehicles while also considering the pedestrians. They also have to distinguish between animals and cyclists as well as other objects that require a specific type of reaction such as slowing down or stopping completely while on the road. This technology can also track moments and the direction of these incoming movements. The other appropriate consideration is the decision-making while the car on the move.

Specific solutions

There are several specific solutions or research strategy to address the perceived need, including a theory of operation, and appropriate recommendations. The first recommendation is to use radar as an alternative. Radar technology brings about the best out of the cars, and it is affordable. Other alternative technologies include radar, vision, Argo AI, and AV. Also, all the weaknesses that are associated with LiDar are not in addition to that the radar, vision, Argo AI, and AV. Self-Drive technology.  When it comes to the rough roads, radar is also the best option even though it does not have the accuracy of the lidar; it has its advantages that can be identified. The theory of operation is that the market will always offer something different in operation. If the product has weaknesses, it is likely that a competitor will take advantage of the weakness.  This is the specific solution that can be recommended for the manufacturers of the self-drive cars. The man alternatives to lidar technology include Radar, vision,Argo AI, and AV. These are specific technologies that can act as alternatives to Lidar and also provide cheaper services.

Conclusion

In inclusion, the increased availability of computing technology and reduced cost of sensing equipment have made the invention of autonomous driving technologies have fastened the device within the last few decades. Various components that make up universe vehicle software have been invented with different alternatives. However, the most common sensor technologies are; radar, lidar, AV, and vision. All these technologies have limitations and advantages that can be associated with them. LiDAR has been dominating the computer technology used in autonomous vehicles. This is because of its efficiency and range of resolution associated with it. However, it has numerous disadvantages and limitations including orange resolution and inability to work in a severe weather condition. It also has zero velocity alongside the high prices that come with their technologies. Safety and reliability have to be considered besides the social and legal acceptance of these free driving methods. Environmental conditions and perceptions have to be considered waiver different robust fusions are leading to a need for further development in this area.

References

Amzajerdian, F., Hines, G. D., Pierrottet, D. F., Barnes, B. W., Petway, L. B., & Carson, J. M. (2018, May). Navigation Doppler Lidar for free ground, aerial, and space vehicles. In CLEO: Applications and Technology (pp. AW3R-3). Optical Society of America.

Filisetti, A., Marouchos, A., Martini, A., Martin, T., &Collings, S. (2018, October). Developments and applications of underwater LiDAR systems in support of marine science. In OCEANS 2018 MTS/IEEE Charleston (pp. 1-10). IEEE.

Levinson, J., Askeland, J., Becker, J., Dolson, J., Held, D., Kammel, S., …&Sokolsky, M. (2011, June). Towards fully autonomous driving: Systems and algorithms. In Intelligent Vehicles Symposium (IV), 2011 IEEE (pp. 163-168). IEEE.

Messikommer, N., Schaefer, S., Dubé, R., & Pfeiffer, M. (2017). Cone Detection using a Combination of LiDAR and Vision-based Machine Learning. arXiv preprint arXiv:1711.02079.

Mollison, M. (2017). High-Speed Autonomous Vehicle for Computer Vision Research and Teaching (Doctoral dissertation, University of Western Australia).

Wolcott, R. W., &Eustice, R. M. (2017). Robust LIDAR localization using multiresolution Gaussian mixture maps for autonomous driving. The International Journal of Robotics Research, 36(3), 292-319.

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