AlphaCar - The Community Token Economy to Reshape the Automotive Industry

Juni 21, 2018

AlphaCar - The Community Token Economy to Reshape the Automotive Industry

AlphaCar - is a facilitator who will create a CTE "Community Token Economy" which is provided for the automotive industry where it is done with the goal of exponentially reducing transaction costs, and an exponential increase in efficiency. in this CTE a customer is also a stake holder. The global automotive industry will turn into a trustworthy market that has a value of $ 10 trillion dollars, This is what can reduce transaction costs. this market is an improvement from the previous market that is the typical lemon market. with this paradigm, the shift will create great wealth.

AlphaCar Technology Work Plan

Blockchain Technology

For a secure transaction, AlphaCar uses a public Blockchain technology to record and keep all information between consumers and auto service providers. It ensures every user get rewarded for their engagement in the community. Due to the issues facing the existing Blockchain technology, the platform invented a data-on-chain dApp (a back-end software that requires no real-time implementation) making it easier and faster for users to access any information.

The Blockchain technology used by AlphaCar comprises of the following :

• Open Platform and Data Storage, Query and Validation: These services are found on the Blockchain system. It is operated by an Inter Planetary File System (IPFS) to keep weak data and record complex data sent to the public Blockchain.

• Batch Asynchronous Processing: This process is run on the Blockchain system to ensure transparency and security of properties. It is also used to resolve complex issues facing the automobile industry.

• Voting System: The platform uses a voting system that gives communities the access to vote on dAPPs. With this, users can also select new logos and algorithm for analyzing service providers.

INTERNET of VEHICLES TECHNOLOGY (IoV)

This aspect of AlphaCar can be divided into three major structures as seen below:

• Smart Driving: To assist drivers in driving, AlphaCar established an Advanced Driver Assistant System (ADAS) in order to ensure adequate safety measures, improve IoV, and low-latency network standard.

• Car Sharing: This was designed majorly for the public transport system. Cars under this system are properly managed, and access to these vehicles at different parks is ensured.

• IoV Structure: This device is used in gathering relevant data which are later transmitted to the cloud for statistical analysis. After this, car owners will be informed of any risky data for security and maintenance purposes.

Data source

There are three important categories of guidance sources:

Internal Data: use units and smartphones to collect using custom guides using custom analysis. For example, we've recently logged in to phone data including GPS, gyroscopes, accelerometers, and magnetic meters, at a frequency of 1 Hz car equipped hardware can offer guides as much as 60Hz. In addition, we are in a position to also collect smartphones status screen, name status, WIFI stand and so on. News gathered may be further processed and smooth into mileage, speed, acceleration, sudden action (braking, slowing down, and rotate), duration of power period, and news about the use of habits, as well as models for the prediction and quantification of accident opportunities. OBD units can also collect unique news about the condition of the car, in proportion to the travel distance, gas mileage, and repair schedule.

External Data Type A: adds a comparable guide to using conditions, proportional to roads, traffic, and weather. Such guidance can be obtained from open lessons as an important point to determine basic correction and model adaptation.

External Data (Type B): adding current guidance is not straight proportional to using condition, proportional to age, gender, occupation, income, marital status, household status, and so on. Such guides can come from other courses, adding cost and purchase channels, and allowing us to build additional models for the profile of fine-grained people, as well as multi-aspect predictive models, especially true ones.

Analytical Methodology

The analytical way of the guide is largely based on multimodal, heterogeneous, dynamic and unstructured modeling data separately and together:

Dynamic Analysis of Unstructured Data

Since the guidance sources vary, it adds guidance on instrument dimensions and textual data recorded, practical guidance as a whole is unstructured, as well as dynamic in nature. Therefore, we want to use multi-variate time-series items for activities and check each type of data.

Multimodal Heterogeneous Data Analysis

We utilize a desktop that is equally antique looking and shiny in figuring out the approach as it is potential for fuse the three important categories of guides brought over. Combining news of unique modalities is always impolite as a consequence of unusual statistics AlphaCar White Paper 10/22 nature and especially nonlinear relationships among the positive aspects of low levels of modalities. Previous work has proven that multimodal search usually adds higher performance to such tasks retrieval, classification, and description. When the unified modalities are temporal, it becomes appropriate for the brand layout to look for multimodal temporal TMLthat can simultaneously combine news from unique sources, and seize the temporal structure in the data. In the preceding five years, some of the deep findings most approaches have found for TML. Early items have been widely established on the use of non-temporal goods that are comparable to deep multimode autoencoders or Boltzmann Machines in RBM are used for aggregated data just some time point in a row. More fresh items have been trying to brand sequential inherent properties of temporal data, eg, Conditional RBMs, Recurrent Temporal Multimodal RBMs RTMRBM, and Multimodal Long-Short-Term Memory networks LSTM. We are recruiting a good brand for TML to simultaneously read the combined representation of the multimodal input, and temporal timing in the data. Moreover, the brand should able to dynamically consider the unique incoming modalities to allow emphasis on useful extras signal (s) and offers noise toughness. The brand must be able to generalize to be different multimodal temporal data types, adding this from smartphones, OBD devices, and external data. The thrilling function of the multimodal temporary guide of the car using the situation is that differences across modalities stem mainly from the use of unique sensors, comparable to smartphones and OBD devices, to capture the same temporal phenomenon. In other words, inner modalities Multimodal temporal guides are usually a unique representation of similar phenomena. To this end, we decided to use a non-supervised Single Correlation Neural Network CorrRNN a model built by the University of Rochester to meet the above desiderata, explicitly capturing the correlation between modalities with the potential of maximizing loss functionality by correlation, as well as minimizing reconstruction-based losses to protect information.

Prospective Applications of AlphaCar

1.Design Together

Designers around the world are collectively designing new cars and sharing gifts

2.Shared R & D

Engineers around the world collectively develop new cars and share gifts

3. Share the car

Distribute cars safely through blockchain and IoV technology

4. Insurance Based Usage

UBI to appreciate good driving behaviour

5. Car Care & Repair

One stop service network survival of the fittest

6. Car Trade

Blockchain, large transparent data rates

7. Car Financing

Large Blockchain data provides effective risk control without trust

Token Introduction and Funding Plan

AlphaCar will difficulty ACAR tokens in accordance with the ERC20 standard. ACAR token is a utility token, which might be used to acquire providers within the AlphaCar CTE. The number of tokens is 10 billion and can by no means be over-issued. The number of tokens may even lower dueto the burning mechanism, wherein no much lower than half of the project’s revenue shall be used to acquire back and burn the ACAR tokens. 40% of the ACAR shall be sold to early dealers of the token, 20% shall be rewarded to community members for his or her contribution, 10% shall be used for CTE development, 20% awarded to the challenge team, 10% to challenge advisors and commercial cooperation. Funds from early ACAR token gross income shall be used for AlphaCar’s world operations

The ACAR token rewards for the venture workforce comply with a four-year vesting schedule, with 25% vested every year. Among them, 25% are vested on the give up of yr 1, and 6.25% are vested at the end of every quarter from yr 2 to yr 4.

Roadmap