Review of Custom Grids for Updated Vehicles on VANET Simulators

VANET deployment and testing is time-consuming and costly. Simulation is a handy and less expensive alternative to real implementation as a workaround. It is required to develop accurate models in order to receive excellent results from a VANET simulation, which difficult operation owes to the complexity of the VANET infrastructure (for example, simulators have to model the navigation models and communication protocols). The network and navigation components, which are the building blocks of contemporary VANET simulators, are described in this section. Simulators are a useful tool for testing VANETs at a minimal cost and without endangering users. However, in order to be helpful and convey trustworthy findings, simulators must be able to simulate new technologies that enter the VANET and enable safety and security procedures. To put it another way, if simulation is a good tool for VANET development it should be enhanced. VANET simulators have been the subject of research since early 2010 [1-4]. They analyze the correctness of VANET's numerous tools like a navigation simulator and network simulator, as well as how these building blocks are connected. The introduction of new network technologies such as 5G, SDN, edge computing, and VANET research as a result of investments in autonomous cars is forcing VANET simulators to re-evaluate their support for these new capabilities. We present an updated evaluation of VANET simulators in this post, highlighting their key features and current support for emerging technologies.

SECURITY STUDY OF ROUTING ATTACKS IN VEHICULAR AD-HOC NETWORKS (AUTONOMOUS CAR)

For the past twenty years, the automotive industry and research organizations have been aiming to put fully autonomous cars on the road. These cars which can be driven without the intervention of a driver, use several sensors and artificial intelligence technologies simultaneously, which allow them to detect the environment in order to merge the information obtained to analyze it, decide on an action, and to implement it. Thus, we are at the dawn of a revolution in the world of transport and mobility, which leads us to ensure the movement of the autonomous car in a safe manner. In this paper, we examine certain attacks on autonomous cars such as the denial of service attack, as well as the impact of these attacks on the last two levels of vehicle autonomy.

Accelerating the Race to Autonomous Cars – A Case Study

Background/Purpose: Every automaker is racing to generate self-driving innovations and some slew of fantastic tech firms and start-ups doing the same. The vehicle industry has a long history of implementing cutting-edge technologies to bring efficient, creative, and reliable vehicles to market, all while working to reduce production costs. Such innovations involve machine learning and computational intelligence, which are essential to automobiles progress. Machine learning (AI) technologies have made the innovative concept of self-driving vehicles an actuality. Today, global automotive rulers such as BMW, Volvo, and Tesla use intelligent automation to enhance production, raise production efficiency, and actually drive secure, extra relaxed, expanding, and increasingly enjoyable. This article provides a comprehensive analysis of Companies in the development of Autonomous vehicles and used ABCD analysis to examine the key parameters. Objective: Analyses the technology and business strategies of the companies in the Race of Autonomous cars. Design/Methodology/Approach: The information for this case study were gathered from various scholarly articles and websites. Findings/Result: The technological details of Artificial Intelligence, Self-driving car companies, laws and restrictions of different companies for using Self-driving vehicles, Autopilot driving features, sales volume and financial expansion, Impact of COIVID-19 on Autonomous vehicles business are studied. The impacts of COVID-19 on the autonomous car business are analysed using the ABCD framework. Originality/Value: The result provides a brief overview of different self-driving vehicle companies and self-driving technology building companies in the competitive race. Paper type: A Research Case study paper - focuses on companies in a race of producing Autonomous vehicles and the growth of those companies.

MEMS-Scanner Testbench for High Field of View LiDAR Applications

LiDAR sensors are a key technology for enabling safe autonomous cars. For highway applications, such systems must have a long range, and the covered field of view (FoV) of >45° must be scanned with resolutions higher than 0.1°. These specifications can be met by modern MEMS scanners, which are chosen for their robustness and scalability. For the automotive market, these sensors, and especially the scanners within, must be tested to the highest standards. We propose a novel measurement setup for characterizing and validating these kinds of scanners based on a position-sensitive detector (PSD) by imaging a deflected laser beam from a diffuser screen onto the PSD. A so-called ray trace shifting technique (RTST) was used to minimize manual calibration effort, to reduce external mounting errors, and to enable dynamical one-shot measurements of the scanner’s steering angle over large FoVs. This paper describes the overall setup and the calibration method according to a standard camera calibration. We further show the setup’s capabilities by validating it with a statically set rotating stage and a dynamically oscillating MEMS scanner. The setup was found to be capable of measuring LiDAR MEMS scanners with a maximum FoV of 47° dynamically, with an uncertainty of less than 1%.

DEVELOPMENT OF INTELLIGENT SELF-BALANCING E-BIKE USING MACHINE LEARNING

The self-driving autonomous cars is becoming an increasingly popular concept all around the world but the area of self-driving two wheelers is still under developed. For developing countries like India, two wheelers are affordable than cars for most of the population. The project aims at developing intelligent self-balancing bike using artificial intelligence because the major problem in developing an autonomous bike is in the area of balancing. Even though there are many working mechanisms available for self-balancing of bike, the implementation of AI will be an edge over others from the point of computational power requirement and the programming complexity incurred. A prototype of the bike was developed with reaction wheel mechanism for self-balancing. The mechanism was fully controlled by AI by preventing the need of explicit programming for balancing which was the earlier technique used in self-balancing bike. Reinforcement learning, a type of machine learning technique is adopted for this purpose. The policy gradient algorithm was used to make the bike learn by itself for balancing. Even though the AI algorithm worked well in the virtual environment (balancing a cart-pole) it fails in the real environment. (i.e. it fails to balance the bike). It is because of the noisy data from the sensor, which gives inaccurate information about the orientation of the bike. The noise in the data is due to the vibration of the body when the reaction wheel rotates. This could be solved if the AI is fed with accurate information about the orientation of the vehicle.

Human factors, artificial intelligence and autonomous cars: Perspectives for a complex implementation

The centuries-old near-inseparable human/automobile relationship faces a revolution thanks to artificial intelligence gradually creating new paradigms in terms of personal urban mobility. Still, would we be prepared to relinquish our vehicle control to autonomous systems? The main objective of this work is to elucidate the main elements of the complex relationship between human factors and artificial intelligence in the development and establishment of autonomous vehicles. Thus, this paper adopted a basic methodology with a qualitative approach with an exploratory objective and technical procedures, as well as technical procedures of a documentary and bibliographic nature. Notice that autonomous systems present plausible functioning in controlled environments, even so, in an environment with several variables and an almost infinite possibility of combinations, enforced the occurrence of failures and compromised the structuring of a mental model, based on human factors, applicable to artificial intelligence. That explains the little importance given to human factors in the planning of human/autonomous machine interactions.

Objects Detection Using Sensors Data Fusion in Autonomous Driving Scenarios

The relatively complex task of detecting 3D objects is essential in the realm of autonomous driving. The related algorithmic processes generally produce an output that consists of a series of 3D bounding boxes that are placed around specific objects of interest. The related scientific literature usually suggests that the data that are generated by different sensors or data acquisition devices are combined in order to work around inherent limitations that are determined by the consideration of singular devices. Nevertheless, there are practical issues that cannot be addressed reliably and efficiently through this strategy, such as the limited field-of-view, and the low-point density of acquired data. This paper reports a contribution that analyzes the possibility of efficiently and effectively using 3D object detection in a cooperative fashion. The evaluation of the described approach is performed through the consideration of driving data that is collected through a partnership with several car manufacturers. Considering their real-world relevance, two driving contexts are analyzed: a roundabout, and a T-junction. The evaluation shows that cooperative perception is able to isolate more than 90% of the 3D entities, as compared to approximately 25% in the case when singular sensing devices are used. The experimental setup that generated the data that this paper describes, and the related 3D object detection system, are currently actively used by the respective car manufacturers’ research groups in order to fine tune and improve their autonomous cars’ driving modules.