Assignment Sample on 7080MAA Connected Autonomous Vehicle Contextualisation
1.0 Electrification
1.1 Power of autonomous vehicle
A midsize sedan requires total power of almost 200 words to reach a high level of automation. In autonomous vehicles, the major additional load is created from the computer of the vehicle, and it can be seen from three to five stepping points for the computer of the vehicle because the computer has to handle some Complex Real-world situation at the time of driving the vehicle on the road. There can be seen in the additional load of the sensors of the car, and these sensors are connected with the 12-volt battery of the car, so it can be seen that this additional load of sensors can increase 50% of the total load on the 12-volt battery (Kanj et al. 2020). So it can be seen that this 12-volt battery is continuously charged by another high voltage battery through the DC to DC Converter system because the car mainly relies on the 12-volt battery to start the engine of the car.
1.2 Central Power Architecture
The power architecture of autonomous vehicles can be seen so that the electrical power and the computational power lie in a single general space in the vehicle. There can be three major types of components. First is the high power battery, second is the power battery, and third is the computation hardware (Doan et al. 2018). The major risk of this type of architecture can be seen because if the design Falls short, it will fail safely because of the Central positioning of the three major control resources of the autonomous vehicle.
1.3 Distributed Power Architecture
The issue, as mentioned earlier, can be reduced by another system called distributed power sources. The vehicle sensor will have to separate 12-volt batteries on each end of the vehicle in the system. In this system, multiple loads are connected with each source of energy, and it can be seen that few power sources are scattering actress the region of the car (Obaid et al. 2021). If the stop condition of the energy source can be seen, the car’s computing system will allow the other sources to compensate for the dysfunctional wire and the power sources. In the distributed power sources, it can be seen that most of the power sources are scattered throughout the vehicle, so reducing the scattering would require some additional wiring system and additional cost also. However, it can be said that this distributed power system configuration is more fail-safe than the centralized power configuration.
2.0 Safety
2.1 Automation Levels
Different levels of Automations are implied into autonomous vehicles for improving the safety features of the vehicle. The level of automation in electric vehicles depends on some major factors, and they are “complexity of the autonomous Technology applied”, “the perception range of the environment”, “the degree of a human driver”, etc. and these are mostly related to the safety of the autonomous vehicles (Papadoulis et al. 2019). The society of safety engineer have defined levels of driving automation in the autonomous vehicle, and they are explained below:
- Level 0 (no automation): Human individual operates all type of driving tasks in the vehicle
- Level 1 (driver assistance): The vehicle is operated by the human but assisted with the automation system.
- Level 2 (personal driving automation): Automatic functions are adopted in the vehicle, but the individual can corporate and monitor the control of the car.
- Level 3 (conditional driving automation): The human individual has to be prepared for operating the vehicle anytime if there is some necessity.
- Level 4 (high driving automation): Under some given conditions, the car can be controlled by the automation system and also the human driver can control the vehicle.
- Level 5 (full driving automation): In all types of conditions, the ultimate system is capable of controlling the car, and the human individual can also control the systems of the AV.
2.2 Errors in the safety of AV
Different types of errors are generated because of the implementation of different types of autonomous techniques in electric vehicles. There can be some critical safety issues if the errors are not handled carefully. If different types of errors are analyzed for the AV, it will be easy to understand the current condition of the safety of autonomous vehicles. From the reports and the literature, it can be seen that the accidents that are reported for autonomous vehicles are way less than traditional vehicles, but it does not mean that autonomous vehicles are safer than human-controlled vehicles.
The accidents which are caused by autonomous vehicles have three major reasons, and they are “perception error”, “decision error”, and “action error”. Errors can be seen because of the improper computation of the automation system installed in the AV for safety (Taeihagh et al. 2019).
2.3 Opportunities of AV
The development of AV technology will eliminate the traditional job opportunities in the faecal manufacturing company, but in reality, more jobs will be created with the development of EV Technology. A lot of Software Hardware vehicle components, sensing devices, and communication systems are required by autonomous vehicles (Ferdowsi et al. 2018). With the development of autonomous vehicles, there can be the development of these sectors also. Human operators can be released with the help of automation in the driving process of autonomous vehicles. Time of transportation can be managed with the most significant weight by the use of autonomous vehicles. The current way of lifestyle will be changed because of artificial physical Technology. The traditional way of driving training and test for the driving license will be changed.
3.0 Ethics
Automation systems of the AV hold a higher safety standard than humans at the time of licensing the automatic cars as Street legal. It has been made like this because autonomous cars have to go through a lot of more complex tests than a new learner driver. In any real-life situation, it is assumed that autonomous vehicles can act ethically and wisely, but as it is the new technology in the market, so they have no track record. Some slight differences can be seen in the legal and regulatory framework for autonomous vehicles. According to Bryant Walker Smith, a fellow of Stanford law has stated, “automated cars are probably legal in the United States, but only because of a legal principle that everything is permitted unless prohibited. That’s to say, and an act is allowed unless it’s explicitly banned because we presume that individuals should have as much liberty as possible. Since, until recently, there were no laws concerning automated cars, it was probably not illegal for companies like Google to test their self-driving cars on public highways” (Fleetwood,2017).
Due to the safety issues of autonomous vehicles, it remains challenging all over the world. The detection and categorization process is not developed in autonomous vehicles, so different perception errors can be seen. Sometimes autonomous vehicles cannot respond to a situation correctly and timely, and for these different types of decisions, errors can be seen in electric vehicles. The Axon taking system of autonomous vehicles has to be improved for better decision management of the vehicle. It is where ethics come into place. If there can be any type of accidents due to these safety roads of the autonomous vehicle, will it be on the vehicle’s driver or the vehicle’s system? And in this type of accident, who will be punished? That is a big question to the Government of the countries. A person states that he was not driving the vehicle, then will it be his or her fault if there are some accidents caused by any type of issue in the automation system of the car. It explains the potential break between the law and the ethics of the country. In the real world, it cannot be seen the alignment of ethics policy and law in the same line (Himmel,2018). Where can we see no existence of the legal framework for autonomous vehicles, so there can we see an opportunity to build a legal framework that will be according to the ethics? At this time, there can be different types of challenges in the policies and the loss of automatic cars, and the government has to ensure the moral Sims for the automated car.
4.0 Design
4.1 Application of AI
Application and the development of relevant artificial intelligence in the autonomous vehicle are an important part of autonomous vehicles’ software and hardware design. The autonomous vehicles process and analyze several data about the road and the paths while on the road and have to process this information to make important decisions to maintain the course of the vehicle. This side information is collected for the information collected from the various sensors and collected information from the network. In most cases, this information is needed to be processed in real-time, and the AI embedded in the system needs to make split-second decisions that will result in the proper safety and maintain the function of the vehicle (Benderius et al. 2017). Hence, the development of the appropriate AI applications to be integrated into the compact systems of autonomous vehicles can be considered challenging, and the development is of paramount importance.
4.2 Changes in vehicle design
The overall design of vehicles has changed quite a bit with the increasing use of technology over the hundred years the Internal combustion vehicles are around. The significant changes in the design can be seen with the increased understanding of aerodynamics and vehicle performance. Similarly, in Autonomous vehicles, the vehicle design is going to be changed significantly (Kong et al. 2017). The primary reason is the electrification of vehicles. There are fewer components in the car’s transmission in an electric vehicle compared to the internal combustion engine. All this extra space and attention is shifted towards increasing the vehicle control and improving the energy capacity of the vehicle (Which is the most space-occupying section of an electric vehicle). The interior space design of the vehicles will also be changing because, in a fully autonomous vehicle, there is very little focus on driving for humans. Also, taking off the vehicle functionality in the vehicle design, there will be an increased dependency on software components of the vehicle.
4.3 Changes in Vehicle human interaction
It can be said that the human interaction of the vehicle will be increased, and the vehicle will be integrated into various smart home devices and the Internet of Things (IoT). In the case of the connected autonomous vehicles, it can be assumed that they will also share data with other home devices via various connectivity options such as WiFi, Cellular networks, or Bluetooth (Xu et al. 2019). Voice assistants will also be an intricate feature of the vehicle. These improved features in the vehicle will help to improve the experience of traveling in an autonomous vehicle. Also, it is required to mention the entertainment features that can be present in these vehicles. It can be suggested that online video streaming and media consumption will be used in the vehicle system.
5.0 Sensors
5.1 Configuration
“Taxonomy and definitions for terms related to an on-road motor vehicle automatic driving systems” standard have discussed six levels of automation in the autonomous vehicles by the “Society of Automotive Engineers (SAE)” (Xu et al. 2018). With the increase of automation level in the autonomous car, there can be an increase in the complexity of the sensors that exist in the car. If level 3 automation has reached any autonomous car, then there can be seen no need for functionality standpoint of the sensor. In level 5 and 6 automation stages, their Canvas into major characters in which the fatal decide that it can operate or not. The two major factors are the backup system and the circumstances. From level 3 automation, the main responsibility of the driving system is to monitor the driving environment of the autonomous vehicle. At level 3 automation in any autonomous vehicle, there can we see the presence of specialized sensors in the cars.
5.2 Different types of sensors
The important sensors which are present in autonomous vehicles are “camera”, “LiDAR”, “Global Navigation satellite system or GPS”, “International measurement unit (IMU)”, “radio detection and ranging (RADAR)” etc. (Kocić et al. 2018). GPS and IMU are used for the navigation system of the vehicle, where LiDAR and RADAR are used for avoiding collision with other vehicles or the pedestrians on the road. For driving the vehicle, different data are collected by the sensors of the vehicle and for analyzing and interpellating those data, a computer system is used in the autonomous vehicles.
5.3 Power of the sensors
The sensors are powered by the 12 w battery of the car, so there can be an additional load on the car’s battery (Chen et al. 2017). The additional load can be e harmful to the autonomous vehicle because it can reduce the life cycle of the batteries and it can also affect the overall range of the autonomous vehicles, different techniques of electrification have been introduced and implemented for reducing the additional load of the battery, and one of the significant technique is called HVAC.
6.0 Connectivity
6.1 Real-Time Network Architecture
Autonomous driving technology will require a higher end of connectivity options to maintain its required functionality on demand. In order to maintain these functionalities will require a much more strong and reliable network infrastructure. These network industries are needed to be capable of responding almost in real-time to reduce the decision-making time of the embedded AI in the autonomous vehicle system. This network will also need to transmit real-time data of the processes generated and information gathered by the vehicles. These proposed network architectures will form clusters of data and datasets in their functional domains and will be connected via a central network infrastructure. The devices of this network cluster will include vehicle sensors, Information transmitters, road safety instructions, smart devices in the vehicles, and the various network applications running in the system, such as maps, navigation, or other entertainment features (Höyhtyä et al. 2019).
6.2 High-Speed Data Connection
The backbone of the real-time network infrastructure is the high-speed data connection. In this section, the term high speed also referred to low latency and a high response frequency in the data connection. The 5G network connection is the first step of developing the network Industrie, which will be the prime requirement of the connectivity issues faced by autonomous vehicles. The high-speed network connection will also be required to have a very high amazon of carrying capacity of information per unit time due to the vast amount of data the vehicle will generate by the several sensors. Manufacturers of autonomous vehicles will be required to increase the bandwidth of data pipes and distributed network structures to meet the new data demand (Jameel et al. 2019). As the following figures explain, for full level 5 autonomy in vehicles, the networks will require at least 25 Gbps data speeds.
6.3 External Connectivity Requirements
In order to maintain all the features in the autonomous vehicles apart from the system connectivity of the network infrastructure, various external connections and third-party apps will also require this same network connection option. Applications such as maps and navigational tools are the primary requirements for maintaining the functionality. Maps are required to identify the proper path planning considering the destination of the users (Zhao et al. 2019). The navigational tools will require the network connectivity options to get the proper paths and driving instruction, obstruction, and traffic information. As mentioned in the previous section, network connectivity will also be required to maintain human interactions with the vehicles.
7.0 Cyber-security
7.1 Overview
Artificial intelligence technology is used by autonomous vehicles, which imply machine learning techniques for collection analysis and transferring the data for making the decisions for the autonomous cars that can be taken by the humans also. Artificial intelligence technology is mainly an IT system, so lots of vulnerabilities and attacks can be seen on the system that can compromise the controlling and functioning of the vehicle. According to Juhan Lepassaar, Executive director of the EU agency for cybersecurity, “When an insecure autonomous vehicle crosses the border of an EU Member State, so do its vulnerabilities. Security should not come as an afterthought, but should instead be a prerequisite for the trustworthy and reliable deployment of vehicles on Europe’s roads” (Taeihagh et al. 2019).
7.2 Vulnerabilities
It can be seen in the use of the artificial intelligence system in autonomous vehicles for continuous recognition of the traffic signs and road markings, detection of the physical estimation of the speed, and planning of the ahead path of the vehicle. Some of the sudden malfunctions can be seen in autonomous vehicles, and that is referred to as the unintentional attacks or vulnerabilities of the autonomous vehicle. These attacks are vulnerable to Attacks that can be poker intentionally (Lim et al. 2018). For this type of international attacks, there can be seen in the interference of The attacks in the system of the car, and safety-critical function can be disrupted by these intentional attacks on the autonomous vehicle. Examples of intentional attacks are Paints on the roads stickers, which contained a stop sign etc.
7.3 Recommendations
The security assessment of the components or machines run by an artificial intelligence system has to be checked regularly throughout the full life cycle of that component. For ensuring the correct behaviour of the vehicle system, validation of the artificial intelligence system is important (Gopalswamy et al. 2018). The threat factors for the autonomous vehicle have to be analyzed regularly, and this enables the understanding of the potential risk and the threats that can arise in the future of autonomous vehicles. Throughout the entire supply chain of autonomous vehicles, proper policies for AI security have to be implemented.
8.0 Autonomy
8.1 Requirements of Autonomy
With the rise in technological aspects in everyday features, the requirements of autonomy in vehicles are also increasing. Primarily the autonomy in vehicles is required to decrease the human dependency on the transportation system. It is a fact that with the introduction of connected autonomy in vehicles, the levels of human-caused road vehicular accidents and decrease the level of traffic caused by human reasons (Fridman,2018). It is also expected that with the introduction of connected autonomous vehicles, the emergency services response will also change and become much more efficient. Plus, it is also a factor that with fully autonomous vehicles, the transportation sector, especially the public transportation service, will change, and big private transportation service providers such as “Uber” will see massive potential profits, and hence these industries are the primary motivator of this industry.
8.2 Challenges of autonomy
The present challenges in connected autonomous vehicles are the phase of infrastructure development and network architecture development. Development of the software components and the AI technologies development is also another major challenge of the autonomy functionality of the vehicles (Mattioli,2018). But for the most important segments of the connected autonomous vehicle infrastructure are the high-speed data connections facilities. The 5G network is still in the development phase in a lot of area specialities in rural and remote regions. Also, due to the high-frequency distribution system of the 5G, one single transmitter cannot spread the data across a vast amount of region and hence the infrastructure development of 5G is not economically suitable for very private network infrastructure developers. Also, the various backlashes and widespread rumours against the technology are a major hurdle in network infrastructure development.
8.3 Evolution of Autonomous technology
There are various key evolution factors which are needed to be considered in the development of autonomous technologies and infrastructure in the present condition. The autonomy levels of vehicles have increased quite a lot in the last decades, and various weather functionalities are planned to be implemented in the upcoming times. One of the mentioned evolution can be noticed in the integration of digital technologies in autonomous vehicles. Various digital technologies and software are presently implemented in-vehicle technology, which is becoming the key factor of vehicle safety and functionality. Also, the evolution of mapping and navigational tools such as “Google Maps”, which guides the user about the path and traffic information, can be seen evolving the transportation sectors (Hancock et al. 2019).
9.0 Human Role
9.1 Role of the driver in Autonomous Vehicle
The role of drivers in an autonomous vehicle is a significant question that is proposed by many relevant researchers in this topic. As discussed previously, there are varying levels of autonomy in an autonomous vehicle that require varying levels of drive intervention and control over the vehicle. Also, it is required to mention that the primary need for autonomy in vehicles is to minimize the human intervention in vehicles (Saleh et al. 2017). So with each increasing level of autonomy, the requirement of driver intervention is also decreased. But with the increased level of automation and less intervention required from the driver, the attention span and situation awareness of the driver and the required skill sets slowly degrades, which can be taken advantage of in case of automation misuse and network intrusion cases of the vehicle.
9.2 Health-related issues
One major unexplored segment of human behaviour in the case of connected autonomous vehicles is the health-related problems that can be caused to the vehicle occupants. Motion sickness is one of the major issues which can be generated in the case of various modes of transportation such as in a plane or a ship. In these cases, documents of the vehicle can experience “symptoms of nausea, dizziness, and other physical discomforts.” The primary cause of motion sickness, in general, is “a conflict between visual and vestibular inputs, loss of control over one’s movements, and reduced ability to anticipate the direction of movement”, all of which can occur in case of the occupants of autonomous vehicles (Nikitas et al. 2019)
9.3 Future of transportation
The evolution of driving technology will change the public transport system. It will also change the law enforcement, insurance, and government policies regarding road safety and transportation conducts, and various other safety measures will be implemented. Transportation service by autonomous vehicles via apps will be much more common and can even change the factors of the traditional car ownership phenomenon (Cho et al. 2017). Hence ride-sharing can be observed to be much more common, and public transportation will see much more participation. In fact, due to the reduced number of accidents and traffic due to autonomous vehicles, public transportation can be considered a much more economically viable solution in a resource-friendly way.
Reference List
Journals
Al-Kanj, L., Nascimento, J. and Powell, W.B., 2020. Approximate dynamic programming for planning a ride-hailing system using autonomous fleets of electric vehicles. European Journal of Operational Research, 284(3), pp.1088-1106.
Benderius, O., Berger, C. and Lundgren, V.M., 2017. The best rated human–machine interface design for autonomous vehicles in the 2016 grand cooperative driving challenge. IEEE Transactions on intelligent transportation systems, 19(4), pp.1302-1307.
Chen, B., Yang, Z., Huang, S., Du, X., Cui, Z., Bhimani, J., Xie, X. and Mi, N., 2017, December. Cyber-physical system enabled nearby traffic flow modelling for autonomous vehicles. In 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC) (pp. 1-6). IEEE.
Doan, V.D., Fujimoto, H., Koseki, T., Yasuda, T., Kishi, H. and Fujita, T., 2018. Simultaneous optimization of speed profile and allocation of wireless power transfer system for autonomous driving electric vehicles. IEEJ Journal of Industry Applications, 7(2), pp.189-201.
Ferdowsi, A., Challita, U., Saad, W. and Mandayam, N.B., 2018, November. Robust deep reinforcement learning for security and safety in autonomous vehicle systems. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 307-312). IEEE.
Fleetwood, J., 2017. Public health, ethics, and autonomous vehicles. American journal of public health, 107(4), pp.532-537.
Fridman, L., 2018. Human-centered autonomous vehicle systems: Principles of effective shared autonomy. arXiv preprint arXiv:1810.01835.
Gopalswamy, S. and Rathinam, S., 2018, June. Infrastructure enabled autonomy: A distributed intelligence architecture for autonomous vehicles. In 2018 IEEE Intelligent Vehicles Symposium (IV) (pp. 986-992). IEEE.
Hancock, P.A., Nourbakhsh, I. and Stewart, J., 2019. On the future of transportation in an era of automated and autonomous vehicles. Proceedings of the National Academy of Sciences, 116(16), pp.7684-7691.
Himmel Reich, J., 2018. Never mind the trolley: The ethics of autonomous vehicles in mundane situations. Ethical Theory and Moral Practice, 21(3), pp.669-684.
Höyhtyä, M., Huusko, J., Kiviranta, M., Solberg, K. and Rokka, J., 2017, October. Connectivity for autonomous ships: Architecture, use cases, and research challenges. In 2017 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 345-350). IEEE.
Jameel, F., Chang, Z., Huang, J. and Ristaniemi, T., 2019. Internet of autonomous vehicles: architecture, features, and socio-technological challenges. IEEE Wireless Communications, 26(4), pp.21-29.
Kocić, J., Jovičić, N. and Drndarević, V., 2018, November. Sensors and sensor fusion in autonomous vehicles. In 2018 26th Telecommunications Forum (TELFOR) (pp. 420-425). IEEE.
Kong, L., Khan, M.K., Wu, F., Chen, G. and Zeng, P., 2017. Millimeter-wave wireless communications for IoT-cloud supported autonomous vehicles: Overview, design, and challenges. IEEE Communications Magazine, 55(1), pp.62-68.
Lim, H.S.M. and Taeihagh, A., 2018. Autonomous vehicles for smart and sustainable cities: An in-depth exploration of privacy and cybersecurity implications. Energies, 11(5), p.1062.
Martínez-Díaz, M. and Soriguera, F., 2018. Autonomous vehicles: theoretical and practical challenges. Transportation Research Procedia, 33, pp.275-282.
Mattioli, M., 2018. Autonomy in the Age of Autonomous Vehicles. BUJ Sci. & Tech. L., 24, p.277.
Nikitas, A., Njoya, E.T. and Dani, S., 2019. Examining the myths of connected and autonomous vehicles: analysing the pathway to a driverless mobility paradigm. International Journal of Automotive Technology and Management, 19(1-2), pp.10-30.
Obaid, M., Torok, A. and Ortega, J., 2021. A Comprehensive Emissions Model Combining Autonomous Vehicles with Park and Ride and Electric Vehicle Transportation Policies. Sustainability, 13(9), p.4653.
Papadoulis, A., Quddus, M. and Imprialou, M., 2019. Evaluating the safety impact of connected and autonomous vehicles on motorways. Accident Analysis & Prevention, 124, pp.12-22.
Pettigrew, S., Fritschi, L. and Norman, R., 2018. The potential implications of autonomous vehicles in and around the workplace. International journal of environmental research and public health, 15(9), p.1876.
Porter, L., Stone, J., Legacy, C., Curtis, C., Harris, J., Fishman, E., Kent, J., Marsden, G., Reardon, L. and Stilgoe, J., 2018. The autonomous vehicle Revolution: Implications for planning/The driverless city?/autonomous vehicles–a planner’s response/autonomous vehicles: Opportunities, challenges and the need for government action/three signs autonomous vehicles will not lead to less car ownership and less car use in car dependent cities–a case study of Sydney, Australia/planning for autonomous vehicles? Questions of purpose, place and pace/ensuring good governance: The role of planners in the development of autonomous vehicles …. Planning Theory & Practice, 19(5), pp.753-778.
Saleh, K., Hossny, M. and Nahavandi, S., 2017, April. Towards trusted autonomous vehicles from vulnerable road users perspective. In 2017 Annual IEEE International Systems Conference (SysCon) (pp. 1-7). IEEE.
Taeihagh, A. and Lim, H.S.M., 2019. Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transport reviews, 39(1), pp.103-128.
Taeihagh, A. and Lim, H.S.M., 2019. Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transport reviews, 39(1), pp.103-128.
Xu, S. and Peng, H., 2019. Design, analysis, and experiments of preview path tracking control for autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 21(1), pp.48-58.
Xu, W., Yan, C., Jia, W., Ji, X. and Liu, J., 2018. Analyzing and enhancing the security of ultrasonic sensors for autonomous vehicles. IEEE Internet of Things Journal, 5(6), pp.5015-5029.
Zhao, L. and Malikopoulos, A.A., 2019. Enhanced mobility with connectivity and automation: A review of shared autonomous vehicle systems. arXiv preprint arXiv:1905.12602.
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