Civil of environmental engineering
Autonomous vehicles
Introduction
Autonomous vehicles refer to the type of automobiles that can drive themselves without the essence of human supervision or input. On the other hand unmanned automobiles are types of vehicles that are either controlled with the aid of a remote or sometimes operate autonomously. They are also referred to as robotic, self-driving, or driverless car (Ferreras, 2014). This type of cars is capable of sensing and navigating the environment without necessarily depending on human input. For instance robotic cars only exist as prototypes and demonstration systems.
To begin with, the important aspect about the autonomous vehicles is the essence of motion planning. On the other hand, handling both the execution as well as the research regarding the collision free pathway is via the execution of certain jobs that in return becomes the ultimate goal for these vehicles. Path planning and path tracking is the main area of motion planning. Computation and searching of collision-free path, considering the geometry of the vehicle ,taking into account the vehicles surrounding obstacles, kinematic constraints of the vehicle and as well as the dynamic constraints is the is the tasks involved with path planning. Similarly, path tracking pertains the actual navigation along a predefined pathway by considering the dynamic and kinematic constraints of the vehicle. This implies that optimal planning is one of the methods regarding the various approaches in path planning of these driverless vehicles. To ensure that this vehicle moves along this pathway, from the start to the target location without colliding with environmental obstacles, computing various kinds of optimization methods are essential too (Mashadi & Majidi, 2014).
Autonomous vehicles equally depend on the technology that enables it to interact with other drivers on the pathway, traffic signals, and also other pedestrians. For instance employing the Google car as an example, makes it easier to focus and explain how these inevitable technology operates and interacts with itself and other vehicles on the road. ‘’Laser range finder’’ that is mounted on the roof of the Google car helps it to interpret the surrounding terrain and display a three-dimensional map. In return these maps that the car is depending on are the Google map which is located by manually driven cars. The robotic car is also designed with different sensors besides the ‘’laser range finder’’ mounted on top of the roof of the car. This include asset of four systems that is mounted on the car pumpers that allows seeing a distance sufficient to deal with the quick traffic on freeways (Swanson., 2014). Additionally on the rear-view mirror, the car contains a camera which it uses to detect traffic lights and global positioning system (GPS) unit and inertia measuring unit. Also mounted is a wheel decoder that is used to determine location of vehicle and keeps track of the vehicles movement.
Nonetheless, Light Detection and Ranging (LIDER) sensors are mainly employed in sensitivity for these cars in that they possess a high speed, accuracy, and range. These characteristics makes the above sensors to be suitable in integration hence providing perception layers of controllers that has the capacity of avoiding collision with unpredicted obstacles (Bouchon et al., 2002).The TBM deals with uncertain and contradictory date problems in an efficient manner. This is in return suited for the processing of sensor data and the fusion of other data from different sources. Surrounding vehicles are represented on a grid of two or three dimensions as mentioned earlier. In this framework the LIDAR sensor aims at scanning a single plane hence the essence of using a two-dimensional grid so that the two axes appear as longitudinal and lateral. This information is essential for these automobiles in that it helps to perform various tasks such as avoiding collision and self-localization. The main advantage of this method is that it enables individuals to reduce the complexity problems in the process of segmenting it and in connection to other detection and tracking activities. Conversely, the ease of integrating it with other modernized sensors assist in enhancing the overall perception of the whole system (Domínguez et al., 2013).
Considering the automotives hardware, these vehicles also uses the custom interface so as to allow direct shifting of the gears, brakes electronic actuation and steering. With the software design, the vehicles driving software mechanism factors in three functional areas; perception, control and as well as planning as discussed above. Organizing vehicles into a platoon is effective in meeting the highway system requirement hence increasing traffic throughput (Yonggui et al., 2014). Equally, different parking scenario for these driverless cars is considered. For the purpose of control and monitoring, a reliable Wi-Fi communication between the back end and the vehicles is essential. In order for the vehicle not to hit the ‘’ white spot’’ without any available network connectivity, a potential QOS requirement of various applications is linked for it. Connectivity maps are therefore used to estimate and represent these network properties. In connection to that, the V- project that was developed by the European Union improves the situation through enhancing the driver to halt the vehicle at selected areas in front of the departure and incoming and to directly hasten the departure at the gate. This necessitates for the unmanned vehicles to become more promising and active as opposed to the manned manual ones (Pögel et al., 2013).
Despite that the evolution of autonomous vehicles is not new, the development of this technology have not enabled these vehicles to complete a substantial amount of distance. The Urban challenge that was established proved that the vehicles safely operate in urban environment and that they have the ability to interact safely with the dynamic environment (N.R.C, 2005). The incorporation of this inevitable technology based on autonomous vehicles and information technology (IT) will make a breakthrough in this field. Combination of high performance cars and as well as the intelligent transportation system (ITS) will produce a powerful tool of reaching new levels concerned with the optimization of urban transportation. An increasing need to provide a safer access to transportation to the elderly and the disabled will still be the aim. This is to imply that mass transit, walking and individual car travel would be efficient and easy. In conclusion it is wise to say that robotic cars are reliable in that their instrumentation and design performs safely that human perception, hence fewer accidents especially during high speed. Thus fewer accidents imply that the health costs are also reduced (Ferreras, 2014).
Present Worth and Annual Cash Flow Analysis
With respect to the evolution of the modern transportation sector, is should be noted that the autonomous vehicles are on its testing phase. This then indicates that it is ultimately difficulty to be in the position of anticipating the actual outcome which is to be realized from such transportation advancements. The only factor which ought to be taken into considerations is estimating the likely magnitude of its impact to the modern economy (Mashadi & Majidi., 2014). On the other hand, additional benefits from the use of autonomous vehicles entail assessing the benefits which might arise from it as a result of higher fuel consumptions rate of the existing automotives.
Regardless of that, it has been estimated that the general mechanism regarding the spacing of the AVs has the potential of reducing it to about 15 percent. In connection to that, the road training platoons mainly facilitates the adaptive braking of the uncontrolled road challenges which enhances effective usage of the existing transportation sector. Despite of the manner in which the autonomous vehicles would have been considered to be safer, the truth is that they are ultimately unsafe because of the absence of human driver in it. The perception which is concerned with this system is known to be having the capacity of driving policy which in return has the potential of delaying its implementation. Additionally, in case the autonomous vehicles would have been held to relatively much higher standards unlike the existing human drivers, the fact is that it costs will be rising to a certain extent which also make the majority of individuals to be unable to purchase them. This implies that a number of steps ought to be taken into consideration in order to account for its liability concerns (Ferreras, 2014).
In the connection to the above considerations, the notion regarding driverless vehicles may seen to be a distant possibility in the transportation sector. Regardless of that, autonomous technology is perceived to be having the capacity of improving exponentially. The reason for that is because some of the features used in them are already used on some of the current vehicles (Lukas, 2017). In connection to that is that such vehicle has the potential of reducing crashes, the easiness of dealing with traffic, improving the fuel economy, reducing packing needs, bringing mobility to those individual doesn’t have the capacity of driving, as well as the over time dramatic changes in the nature of travelling. In return, the impact of this is that it will have quantifiable and real benefits to any economy which will make use of this technology.
In connection to that, another point which had to be taken into consideration is that the annual economic benefits which are perceived to be arising from this technology mainly ranges between $25 billion with at least 15% market penetration. This is to imply that in the process of including other broader returns and some of the highly penetrating rates, the truth is that the autonomous vehicles will offer the potential of saving the economy of such a country. Regardless of the fact that this financial perspective doesn’t include the associative costs and other externalities, the goodness is that there will be an extensive or dramatic changes which will be encountered I both the safety and nature of the modern transportation sector (N.R.C, 2005).
This shows that the potential benefits remains to be substantial to the economy regardless of the significant barriers which impedes its full implementation as well as other mass-market penetration. The initial expenses the autonomous vehicles will be costing is estimated to be extremely unaffordable to the majority of the developing countries unlike the developed ones. It should be noted that the majority of the states are currently trying to pursue their testing and licensing requirements. This step has the potential of leading to desperate patchwork of requirements and regulations despite of the federal guidance offered (Swanson., 2014).
Finally, the general framework regarding the liability of the autonomous vehicles is typically absent. This results to the creation of uncertainty in the process of encountering accidents (Vant︠s︡evich et al., 2015). Therefore, security concerns ought to be examined typically from the regulatory standpoint to the travelling and privacy issues. Despite the fact that the majority of the automotive manufactures have been extensively motivated by this technology, there is the need of ensuring that policy makers are in the position of supporting its research. This should also be connected to the need of understanding the manner in which it will affect the transportation industry (Lukas, 2017). The reason for that is because the benefits which are perceived to be arising from this technology is typically autonomous to the modernization of the automotive industry.
Work cited
Bouchon-Meunier, B., Gutiérrez-Ríos, J., Magdalena, L., & Yager, R. R. (2002). Technologies for Constructing Intelligent Systems 2: Tools. Heidelberg: Physica-Verlag HD.
Domínguez, R., Alonso, J., Onieva, E., & González, C. (2013). A transferable belief model applied to LIDAR perception for autonomous vehicles. Integrated Computer-Aided Engineering, 20(3), 289-302. doi:10.3233/ICA-130433
Ferreras, L. E. (2014). THE DRIVERLESS CITY. Civil Engineering (08857024), 84(3), 52-55.
In Vant︠s︡evich, V. V., In Blundell, M., & NATO Advanced Study Institute on Advanced Autonomous Vehicle Design for Severe Environments. (2015). Advanced autonomous vehicle design for severe environments.
Lukas, N, (2017). Corporate Mobility Breakthrough 2020. Troubador Publishing Ltd, 2017
Mashadi, B., & Majidi, M. (2014). Global optimal path planning of an autonomous vehicle for overtaking a moving obstacle. Latin American Journal Of Solids & Structures, 11(14), 2555-2572.
National Research Council. (2005). Autonomous vehicles in support of naval operations. Washington: National Academies Press.
Pögel, T., Timpner, J., Rottmann, S., & Wolf, L. (2013). Estimation of Vehicular Connectivity in Autonomous Parking Scenarios. PIK - Praxis Der Informationsverarbeitung Und Kommunikation, 36(4), 243-248. doi:10.1515/pik-2013-0026
SWANSON, A. R. (2014). "SOMEBODY GRAB THE WHEEL!": STATE AUTONOMOUS VEHICLE LEGISLATION AND THE ROAD TO A NATIONAL REGIME. Marquette Law Review, 97(4), 1085-1147
Yonggui, L., Huanli, G., Bugong, X., Guiyun, L., & Hui, C. (2014). Autonomous coordinated control of a platoon of vehicles with multiple disturbances. IET Control Theory & Applications, 8(18), 2325-2335. doi:10.1049/iet-cta.2014.0172
