scholarly journals Securing Personal Data using Block chain in Autonomous Vehicle

2019 ◽  
Vol 8 (4S2) ◽  
pp. 760-763
Keyword(s):  
Science Fiction ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Personal Data ◽  
Testing Stage ◽  
The World ◽  
Driverless Cars ◽  
Block Chain ◽  
Manufacturing Stage

Autonomous vehicles like Driverless cars are seen only in science fiction movies but in 2019 they are becoming a veracity and reality. People all around the world are excited to watch the driverless car in reality. Complete driverless car is still at an advanced testing stage. An autonomous vehicle promises to improve traffic safety while at the same time it must not be prone to hacking. Even though the existence of the autonomous car is in reality there is a possibility of hackers to hack the vehicle and retrieve the precious data. To stop this kind of hacking we propose a block chain technique that safe guards the data that is fed to the autonomous car during the manufacturing stage and this cannot be deleted without proper permission.

scholarly journals Lane-deviation penalty formulation and analysis for autonomous vehicle avoidance maneuvers

2021 ◽  
pp. 095440702110079
Author(s):  
Pavel Anistratov ◽  
Björn Olofsson ◽  
Lars Nielsen
Keyword(s):  
Objective Function ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Emergency Situation ◽  
Change Scenario ◽  
Initial Speed ◽  
Objective Functions ◽  
Double Lane Change ◽  
Tire Friction

Autonomous vehicles hold promise for increased vehicle and traffic safety, and there are several developments in the field where one example is an avoidance maneuver. There it is dangerous for the vehicle to be in the opposing lane, but it is safe to drive in the original lane again after the obstacle. To capture this basic observation, a lane-deviation penalty (LDP) objective function is devised. Based on this objective function, a formulation is developed utilizing optimal all-wheel braking and steering at the limit of road–tire friction. This method is evaluated for a double lane-change scenario by computing the resulting behavior for several interesting cases, where parameters of the emergency situation such as the initial speed of the vehicle and the size and placement of the obstacle are varied, and it performs well. A comparison with maneuvers obtained by minimum-time and other lateral-penalty objective functions shows that the use of the considered penalty function decreases the time that the vehicle spends in the opposing lane.

scholarly journals Disruptive Impacts of Automated Driving Systems on the Built Environment and Land Use: An Urban Planner’s Perspective

2019 ◽  
Vol 5 (2) ◽  
pp. 24 ◽  
Author(s):  
Yigitcanlar ◽  
Wilson ◽  
Kamruzzaman
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Capital Investment ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Urban Transport ◽  
Current Status ◽  
Smart Mobility

Cities have started to restructure themselves into ‘smart cities’ to address the challenges of the 21st Century—such as climate change, sustainable development, and digital disruption. One of the major obstacles to success for a smart city is to tackle the mobility and accessibility issues via ‘smart mobility’ solutions. At the verge of the age of smart urbanism, autonomous vehicle technology is seen as an opportunity to realize the smart mobility vision of cities. However, this innovative technological advancement is also speculated to bring a major disruption in urban transport, land use, employment, parking, car ownership, infrastructure design, capital investment decisions, sustainability, mobility, and traffic safety. Despite the potential threats, urban planners and managers are not yet prepared to develop autonomous vehicle strategies for cities to deal with these threats. This is mainly due to a lack of knowledge on the social implications of autonomous capabilities and how exactly they will disrupt our cities. This viewpoint provides a snapshot of the current status of vehicle automation, the direction in which the field is moving forward, the potential impacts of systematic adoption of autonomous vehicles, and how urban planners can mitigate the built environment and land use disruption of autonomous vehicles.

scholarly journals Quick Routing in Autonomous Vehicles using Boosting Algorithms

2019 ◽  
Vol 9 (1S3) ◽  
pp. 539-542
Keyword(s):  
Artificial Intelligence ◽  
Shortest Path ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Fuel Efficiency ◽  
Time Span ◽  
Minimum Time ◽  
Novel Technique ◽  
Boosting Algorithms

Self-driving automobiles are understandably the most attention grabbing utility of artificial intelligence. Until recently, we have just considered the prototypes of these cars in Sci-fi movies, with the whole thing else left to our imagination. But with advances in technology, this super notion has acquired a lifestyles of its own. Autonomous vehicle promises to improve traffic safety while at the same time, it must increase the fuel efficiency, reduce congestion and arrive to the destination at a minimum time span. We propose a novel technique to boost the algorithm to take the shortest path while the vehicle is in movement.

Risk Analysis of Autonomous Vehicles in Mixed Traffic Streams

2017 ◽  
Vol 2625 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Parth Bhavsar ◽  
Plaban Das ◽  
Matthew Paugh ◽  
Kakan Dey ◽  
Mashrur Chowdhury
Keyword(s):  
Traffic Safety ◽  
Failure Probability ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Fault Tree ◽  
Communication Technologies ◽  
Transportation Infrastructure ◽  
New Combination ◽  
Motor Vehicles ◽  
Mixed Traffic

The introduction of autonomous vehicles in the surface transportation system could improve traffic safety and reduce traffic congestion and negative environmental effects. Although the continuous evolution in computing, sensing, and communication technologies can improve the performance of autonomous vehicles, the new combination of autonomous automotive and electronic communication technologies will present new challenges, such as interaction with other nonautonomous vehicles, which must be addressed before implementation. The objective of this study was to identify the risks associated with the failure of an autonomous vehicle in mixed traffic streams. To identify the risks, the autonomous vehicle system was first disassembled into vehicular components and transportation infrastructure components, and then a fault tree model was developed for each system. The failure probabilities of each component were estimated by reviewing the published literature and publicly available data sources. This analysis resulted in a failure probability of about 14% resulting from a sequential failure of the autonomous vehicular components alone in the vehicle’s lifetime, particularly the components responsible for automation. After the failure probability of autonomous vehicle components was combined with the failure probability of transportation infrastructure components, an overall failure probability related to vehicular or infrastructure components was found: 158 per 1 million mi of travel. The most critical combination of events that could lead to failure of autonomous vehicles, known as minimal cut-sets, was also identified. Finally, the results of fault tree analysis were compared with real-world data available from the California Department of Motor Vehicles autonomous vehicle testing records.

Autonomous Vehicles

2021 ◽  
pp. 1-11
Author(s):  
Yair Wiseman
Keyword(s):  
Autonomous Vehicles ◽  
Steering Wheel ◽  
The World ◽  
Driverless Cars ◽  
One Step ◽  
Remote Controller

The first car was invented in 1870 by Siegfried Marcus. Actually, it was just a wagon with an engine but without a steering wheel and without brakes. Instead, it was controlled by the legs of the driver. Converting traditional vehicles into autonomous vehicles was not just one step. The first step was just 28 years after the invention of cars, that is to say 1898. This step's concept was moving a vehicle by a remote controller. Since this first step and as computers have been becoming advanced and sophisticated, many functions of modern vehicles have been converted to be entirely automatic with no need of even remote controlling. Changing gears was one of the first actions that could be done automatically without an involvement of the driver, so such cars got the title of “automatic cars”; however, nowadays there are vehicles that can completely travel by themselves although they are not yet allowed to travel on public roads in most of the world. Such vehicles are called “autonomous vehicles” or “driverless cars”.

scholarly journals Multipurpose Agriculture Robot

2021 ◽  
Vol 9 (VII) ◽  
pp. 3062-3065
Author(s):  
Dipali Muntode
Keyword(s):  
Autonomous Vehicles ◽  
Solar Power ◽  
Autonomous Vehicle ◽  
Data Input ◽  
Ir Sensor ◽  
The People ◽  
The World ◽  
Seed Sowing ◽  
Autonomous Machine ◽  
Whole Systems

The development and the fabrication of the robot which can dig the soil, put the seeds, leveler to close the mud and sprayer to spray water, these whole systems of the robot works with the battery and the solar power. More than 40% of the population in the world chooses agriculture as the primary occupation, in recent years the development of the autonomous vehicles in the agriculture has experienced increased interest. The vehicle is controlled by Relay switch through IR sensor input. The language input allows a user to interact with the robot which is familiar to most of the people. The advantages of these robots are hands-free and fast data input operations. In the field of agricultural autonomous vehicle, a concept is been developed to investigate if multiple small autonomous machine could be more efficient than traditional large tractors and human forces. Keeping the above ideology in mind, a unit with the following feature is designed: Ploughing is one of the first steps in farming. During this process we till the land and make it ready for the seed sowing. By tilling we mean that a plough will be used which will have teeth’s like structure at the end and will be able to turn the top layer of soil down and vice-versa. Seed sowing comes next where the seeds need to be put in ground at regular intervals and these needs to be controlled automatically. Limiting the flow of seeds from the seeds chamber is typically doing this. Mud leveler is fitted to close the seeds to the soil and to level the ground.

Autonomous Vehicles

2022 ◽  
pp. 878-889
Author(s):  
Yair Wiseman
Keyword(s):  
Autonomous Vehicles ◽  
Steering Wheel ◽  
The World ◽  
Driverless Cars ◽  
One Step ◽  
Remote Controller

The first car was invented in 1870 by Siegfried Marcus. Actually, it was just a wagon with an engine but without a steering wheel and without brakes. Instead, it was controlled by the legs of the driver. Converting traditional vehicles into autonomous vehicles was not just one step. The first step was just 28 years after the invention of cars, that is to say 1898. This step's concept was moving a vehicle by a remote controller. Since this first step and as computers have been becoming advanced and sophisticated, many functions of modern vehicles have been converted to be entirely automatic with no need of even remote controlling. Changing gears was one of the first actions that could be done automatically without an involvement of the driver, so such cars got the title of “automatic cars”; however, nowadays there are vehicles that can completely travel by themselves although they are not yet allowed to travel on public roads in most of the world. Such vehicles are called “autonomous vehicles” or “driverless cars”.

scholarly journals Autonomous Driving in Traffic: Boss and the Urban Challenge

AI Magazine ◽  
2009 ◽  
Vol 30 (2) ◽  
pp. 17 ◽  
Author(s):  
Chris Urmson ◽  
Chris Baker ◽  
John Dolan ◽  
Paul Rybski ◽  
Bryan Salesky ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Software System ◽  
Intelligent Software ◽  
The World

The DARPA Urban Challenge was a competition to develop autonomous vehicles capable of safely, reliably and robustly driving in traffic. In this article we introduce Boss, the autonomous vehicle that won the challenge. Boss is complex artificially intelligent software system embodied in a 2007 Chevy Tahoe. To navigate safely, the vehicle builds a model of the world around it in real time. This model is used to generate safe routes and motion plans in both on roads and in unstructured zones. An essential part of Boss’ success stems from its ability to safely handle both abnormal situations and system glitches.

scholarly journals Assessment of external interface of autonomous vehicles

2021 ◽  
Vol 61 (6) ◽  
pp. 733-739
Author(s):  
Adam Orlický ◽  
Alina Mashko ◽  
Josef Mík
Keyword(s):  
Decision Making ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Visual Cues ◽  
Autonomous Vehicle ◽  
Gaze Direction ◽  
Eye Tracker ◽  
Communication Interface ◽  
External Communication ◽  
External Interface

The paper deals with the problem of a communication interface between autonomous vehicles (AV) and pedestrians. The introduced methodology for assessing new and existing e-HMI (external HMI) contributes to traffic safety in cities. The methodology is implemented in a pilot experiment with a scenario designed in virtual reality (VR). The simulated scene represents an urban zebra crossing with an approaching autonomous vehicle. The projection is implemented with the help of a head-up display – a headset with a built-in eye tracker. The suggested methodology analyses the pedestrian’s decision making based on the visual cues – the signals displayed on the autonomous vehicle. Furthermore, the decision making is correlated to subjects’ eye behaviour, based on gaze-direction data. The method presented in this paper contributes to the safety of a vehicle-pedestrian communication of autonomous vehicles and is a part of a research that shall further contribute to the design and assessment of external communication interfaces of AV in general.

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