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Machine Intelligence Unleashes Autonomous Transportation

The current decade is not only significant for automobile manufacturer for commercializing autonomous vehicles. It also witnesses the landmark achievement of autonomy in the ride-sharing industry. Artificial intelligence laid the foundation stone to achieve these feats. Machine intelligence in transportation is a crucial component of future urban mobility.

The transportation monetization with AI will be valued at $750 billion by 2030 if it continues to grow at the current rate. Inexperienced researchers, as well as entrepreneurs, should know the steps that enable the autonomous cars to drive without human assistance.

Various components integrate to achieve a common milestone of uninterrupted mobility.

A completely autonomous vehicle is equipped with multiple sensors intended to achieve distinct tasks. The processors analyze the data from various sensors to enable smooth movement. This data integration occurs in real-time to allow a vehicle to make swift and appropriate decisions.

Computer Vision Sensors

Most of the current self-driving vehicles primarily use a single camera mounted on the windscreen. However, many auto manufacturers are introducing multiple cameras to increase visibility. The analysts believe that single camera version may work well on highways with less congestion. However, the urban downtowns and narrow pathways require far greater visibility.

Latest models of autonomous vehicles use a combination of four to six cameras covering 360° around the vehicle. Computer Vision (CV) algorithms power these cameras to detect objects ahead and around them.

The CV libraries use various machine learning techniques to detect the objects. The developers can customize the libraries to make a distinction between different objects. They need to predefine the region of interest (ROI) before algorithm undergoes supervised training.

Radar Sensors

RADAR is the current standard for assessing the distance and volume of objects around a vehicle. It uses medium and short-range radio waves which share the information of nearby objects after striking them.

The vehicles equipped with RADAR no longer require side-mirrors because any vehicle can estimate distance of other cars by using radio waves. Apart from lane changing assistance, the RADAR also enables the vehicles to maintain a safe distance, and park efficiently.

Currently, machine learning in transport management works in conjuncture with 24-GHz RADAR. However, 122 GHz technology is in the final testing phase and may soon be available by early next year. Besides, observers expect monolithic System-on-Chip technology to make way into the industry to integrate baseband functions along with RADAR.

LiDAR Sensor

While RADAR is already increasing the efficiency of self-driving vehicles, the LiDAR sensor set to completely transform them. Instead of radio waves, LiDAR uses laser light. Since the wavelength is relatively much shorter than, the system should comprise an intensely sensitive receiver apart from the transmitter. The shorter wavelength ensures greater frequency which allows the laser to penetrate the object.

This penetration assists the LiDAR system in modeling a 3D sketch of the object. High frequency also allows the system to work effectively under inclement weather conditions. In contrast, the environment significantly affects the efficiency of RADAR.

InnovizOne – one of the winners of CES Best of Innovation Awards – is a LiDAR technology equipped with the ability to detect objects as far as 250 meters from the vehicle. Owing to LiDAR technology, the device is resilient to harsh weather conditions. This gadget can track objects in real-time apart from detecting lanes. Moreover, it can also classify objects boosting the machine learning in transportation engineering.

Such inventions ensure that auto manufacturing giants will be able to reduce accidents up to a remarkable degree. The machine learning in transportation business will soon allow complete elimination of vulnerability. Since 94% of car accidents involve human error, autonomous cars will significantly bring down the percentage of crashes.

Machine Learning and Coordinate Resolution

The transport industry is one of the chief stakeholders of GPS technology. Every driver with smartphone access may find the one’s pinpoint location in the remotest regions using the GPS. It does not require internet connectivity.

Machine learning enhances GPS use by providing highly-efficient route optimization. Correct implementation enables the optimization algorithms to detect narrow streets and parking lots too. The AI in transportation ensures that the vehicle makes better route selection decisions without affecting the mobility of nearby vehicles.

Finally, the data from all the sensors, cameras and GPS capacitates the vehicle to drive without driver’s assistance. Level-two autonomy also features cruise control where the vehicle moves without interference from the driver. However, cruise control can only be enabled in certain conditions.

Challenges and Notable Breakthroughs

Although the application of machine learning in transportation fared very well, yet administrators and auto-manufacturers need to overcome a few shortcomings too.

  • The RADAR sensors with 4-GHz bandwidth for 79-GHz frequency would be the ideal case for smooth running of traffic. However, most of the countries currently allow 77-GHz frequency with 1-GHz bandwidth. Nevertheless, regulatory framework is under development with a breakthrough expected soon.
  • The improvement in LiDAR technologies is enabling the manufacturers to use the alternative option of RADAR. The logistics giant DHL announced the use of AI transportation logistics to inducing more stability to the fleet industry.
  • Up to a few years back, consumers were reluctant to using autonomous vehicles for fear of collision. However, they are rapidly gaining trust. The introduction of Waymo in Arizona is a massive development causing a remarkable enthusiasm among travelers to utilize autonomous ride-hailing service.
  • The governments around the developed world are devising a comprehensive framework for autonomous vehicle industry. Most of them already allow incorporation of self-driving vehicles in for urban mobility.
  • Although contemporary transportation systems lack a shared environment, yet upcoming times are set to witness the distributed mobility mechanism controlled from centralized locations. This will allow efficient use of vehicles and will prevent traffic congestion.

Machine intelligence in transportation is very close to achieving an autonomous urban mobility model. The rest of the tasks are only a matter of small time because most of the testing and requisite legislation has already been done.

Meanwhile, investors have an unparalleled opportunity at their disposal to become one of the pioneers of autonomous ride-sharing service. Regardless of the amount of technical understanding, you can be the industry leader too. Sign up today to get going.

By Shahrukh Satti 0 Comments

Future Transportation Trends Depict Dominance of Smart Vehicle

The current wave of industrial revolution redefines multiple industries. Many developed countries are working on raising smart cities with intelligent robots working in place of humans. The most notable factor of a smart city is the means of transportation and commuting. By assessing the research and development of recent times, one can forecast the future transportation trends.

This forecast does not depict the trends of distant future. Instead, the inhabitants of developed nations will witness the CASE vehicles roaming on the highways and narrow streets. The CASE – short for Connected, Autonomous, Shared and Electric – vehicles are the essence of prospective smart cities.

The consumers and entrepreneurs will not have to wait much to observe the CASE vehicles. Many companies are inching closer to reveal their products allowing the investors to revolutionize transport industry.

Since many investors are preparing to flow their money in this revolutionary idea, the aspirants should learn how the CASE concept will evolve the industry.

CASE vehicles will transform the Transportation in multiple ways

Economic Sustainability

The future transportation systems will ensure a three-way economic sustainability for investors, commuters and transport administrators.

Sustainability for Investors

Investment in electric vehicles significantly reduces the costs of operation. The US Department of Energy reveals that the fuel cost of a combustion engine is more than twice of that of electric vehicles. The investors can save millions of dollars annually with efficient fuel consumption. These cost-effective vehicles depicting future trends in public transportation move entirely on charged- battery.

The DOE also reports that the maintenance of EVs cost 35% less than that of the combustion engine. Moreover, the costs of EV maintenance are reducing with time as the number of parts is lessening.

Sustainability for commuters

The travelers and commuters will optimize their daily tasks by reducing the time and costs of traveling. The ability to pool the cars by multiple commuters will divide the fare. In fact, Uber and some other ride-hailing services already offer the option of car-pooling.

The commuters will not face the menace of traffic while commuting by CASE vehicles. An autonomous car is able to locate and identify any object from an approximately 250m distance. If the object is another CASE car, the two vehicles can also communicate with each other to adjust their relative speeds. Thus, the bottle-necks and traffic jams will be things of the past.

Environmental Sustainability

The environmental campaigns are strongly supporting the large-scale production of electric vehicles for transportation and commuting. Research indicates that cars with combustion engines emit three times as much CO2 as EVs.

Some buyers were reluctant to purchase electric cars owing to a severe deficit of charging stations. Nevertheless, governments are paying careful attention to increase the number of chargers to meet demand by the end of 2019.

Evolution of Transport Administrative Setup

The contemporary transportation requires extensive management on multiple fronts. In most cities, an entire section of traffic police maintains the flow of traffic and identify the violators.

Many urban administrations are implementing intelligent cameras which identify the vehicle and share the owner’s information with relevant authorities. The database connected with these cameras generates and sends an automated ticket to the offenders. This mechanism is still in the rudimentary stages, but experts believe that it will mature in less than a year.

The autonomous vehicles will further assist the administrators by eliminating the damages and fatalities due to accidents. Research unveils over 1.25 million annual worldwide casualties due to human errors. Analysts label the current status of self-driving cars as level 4.

Level 1 and 2 involved very limited automation as drivers controlled most of the functions. The subsequent level required only occasional intervention from humans while the vehicle was able to control every function. There is essentially no intervention from humans in Level 4. The vehicle requires supervision only in extremely rare cases. This stage is nearing its conclusion.

Final and forthcoming level will enable the cars to entirely decide in any situations. This level may take some time to evolve, but it will not affect the usual operations. Thus, the connected, autonomous and shared environment is highly effective for administrators.

Transport Business Development

The ride-sharing industry is the chief stakeholder of CASE integration in vehicles. Many transport, commute, and ride-hailing companies are investing a significant amount of money on what some call smart transportation system.

Uber is projecting a ride-pooling service with fully autonomous vehicles by the end of 2020. The company has also revealed the plans to introduce VTOL CASE aircraft ride-hailing. It forecasts to start the service in 2023. The analysts expect it to rule the future trends in air transportation. The aircraft called Nexus is a product of Bell which is in partnership with Uber.

Ford also plans to initiate autonomous ride sharing around the same time as Uber. Unlike Uber, the auto manufacturer will use their vehicles. Google’s parent company Alphabet already started their CASE ride-hailing service in Arizona. The service ensures level 3 autonomy where drivers sit behind the wheel to intervene once alerted by vehicle software.

The fresh investors can benefit as much as the established ones. An investor needs to make collaborations with leaders of multiple industries. For instance, collaboration with the auto manufacturer will ensure the acquisition of vehicles on discounted rates.

Moreover, the on-demand taxi concept does not require ownership of the vehicle. Instead, other investors may offer their vehicle to get a share of profit. The only expenditure is the development of an efficient mobile app which enables the companies to monitor and customers to book rides. Thus, an entrepreneur can start the ride-sharing business with little investment.

It is notable that the development is unlikely to result in unemployment hike. While the autonomous software will replace drivers, scores of alternate jobs will be created to govern the future transportation systems.

Act Now!

All these indicators highlight the significance of CASE vehicles in changing the future transportation trends. This is the appropriate time to invest as the smart industry is emerging. Careful planning and efficient implementation can make any startup as one of the pioneers of smart ride-hailing.

To initiate building the ride-hailing software, you can start your free trial right now.

By Shahrukh Satti 0 Comments

Age of Transition to Smartphones from In-Car-System

Science-fiction novels and movies from half a century back depicted intelligent vehicles. The characters were able to operate such vehicles with compact devices. Although some tech-savvy reviewers welcomed these ideas as a correct prediction of future in-car-system, yet many debunked them while labeling them as fictitious concepts.

Astonishingly, the contemporary technology is ahead of what was forecasted in those scripts. Many vehicle-owners are still unaware of the current technological depth. The past scripts exhibited a range of separate devices embedded in the vehicle which allowed the riders to operate various functions through voice commands or by tapping the screens or buttons.

Each of these technologies is not only present in modern vehicles but can be integrated into a highly compact setting, in essence – the smartphone. Very few stories demonstrated the ability to operate a vehicle while sitting thousands of miles away from it. Regardless of the age and model of vehicle, one can have a smartphone app to control the operations of unlocking, music/radio playing, navigation, and a bunch of other cool functionalities.

Ride-Hailing and Smartphone Revolution

The biggest stakeholders of this technology are the on-demand taxi apps like Uber. The ability to acquire taxi services by a few easy taps on one’s smartphone is a remarkable relief for consumers. The ability does not only provide the service to users at their doorstep but ensure security, time management, and efficient fare calculation mechanism as well.

Vehicle owners and cab service providers love this smartphone capability. However, there are very few tech companies which offer development of applications with such features. Thus, the entrepreneurs have the chance to move ahead and fill the void by developing the perfect mobile app.

However, before investing one’s time and the money, it is plausible to run the requirement analysis for transition to smartphones from in-car-system and determine the specific business needs.

Entrepreneurs face the following business challenges.

1. Scope of System

The scope defines the depth and breadth of software. An application can be as simple as comprising only a few pages or excessively complicated to incorporate additional functionalities. The investors should try to keep the system as concise as possible. This way, they can save money and prevent users from finding the application an overly complex one.

The success of Uber reflects in the fact that the interface is easy-to-use but comprehensive at the same time. The essential features should not be traded-off for simplicity.

2. Competition Analysis

The on-demand service is proving to be one of the most successful investments in every region of the world. Still, the new competitors need to assess the established businesses, their packages, and customer relationship management. Although the business keeps the potential to grow despite having flaws, yet entrepreneurs can beat their rivals only by outperforming them.

A successful analysis involves online surveys to determine customer satisfaction levels and their demands. Some of the demands – including a significant drop in fares – will be hard to address but many of them are agreeable. Satisfying the customer needs will rapidly flourish the business.

3. Region of Operations

Most of the world regions, including the developed countries, still lack the on-demand services. The customers end up acquiring poor on-demand services in some areas because the companies do not pay attention to customer satisfaction owing to the lack of competition. The entrepreneurs should consider the following points while selecting the region.

  • Lack of provision of appropriate services by the established businesses.
  • The absence of service in a high-demand region.
  • The index of ease of doing business. This is crucial because the businesses suffer from lengthy registration process and/or law and order situation in some regions.

4. Application Development

Once the scope, competitors and operational region analysis are done, the investors should choose between acquiring a developer and developing the application oneself. If the entrepreneur is skillful and experienced enough, it is okay to give the development a go. However, if there is no prior experience, it would be unwise not to seek the expert advice and services.

The experienced development teams do not only ensure the integration of required features but also enhance the efficiency. A taxi dispatch solution with every feature that a customer may desire will fail to impress the user if it is inefficient. The inefficiency reflects in the form of delays in request processing, data breaches or frequent crashes. No investor would want an application failing after investing a considerable sum of monetary and temporal resources.

5. Enterprise Registration

Every country has a unique set of the enterprise registration process. The business-friendly states assist the startups whereas some states place a range of restrictions which discourage investors. The aspirant business owners should study the registration requirements at the earliest and have their company registered as per the local law. Any negligence in registration may put the business liable to penalties and, ultimately, to termination.

6. Marketing

The marketers need to inform consumers about the availability of a new startup with all of their desired features. Effective marketing of a startup grows it quickly. If the startup is sharing the market with other established companies, more extensive marketing will be required. The entrepreneurs need to make sure that the consumers of existing services attract to their newly inducted company.

Publicity is a way of informing the public about why your startup is better than the rivals. Social media print and electronic media and brochures are some of the traditional marketing strategies. Some new marketing domains including neuromarketing are also making huge gains. The selection of marketing strategy rests on the investors.

Are You an Aspirant?

Making a firm decision is the first but most significant milestone in the path to owning a flourishing business. The selection of industry after careful analysis considerably increases the probability of success. However, the lack of requirement elicitation or poor development and testing may lead to failure. Therefore, the guidance from industry experts is vital.

Cabstartup assists entrepreneurs in transforming their business ideas into reality. Our expert team can help you in every phase including planning, development, testing, and marketing. If you intend to own the most promising startup, sign up today.

By Shahrukh Satti 0 Comments

How an AI Powered Cab Dispatch System Brings Efficiency To Your Business?

Artificial Intelligence (AI) and Machine Learning (ML) are the most frequently used terms in every industry today. They have enormous advantages as tools simplifying complex and time-consuming processes. They also improve the efficiency of central business functions at both basic and advanced level.

For example, AI is used to increase the time and cost efficiency of the HR function by allowing the application screening and query resolution processes to it.

The technology has also found application in medicine for planning treatment plans and even for foretelling the chance of death during surgical procedures. Commercial airlines use AI as an autopilot on their flights.

The utility and application of AI and Machine Learning have now exceeded any realm of uncertainty. And considering the technology-driven nature of on-demand taxi businesses, it was only a matter of time before AI was driven into service in this business.

Taxi Apps with Artificial Intelligence on Mobile:

Through the use of its subfields such as Machine Learning, Deep Learning, and Predictive Analysis, AI has let taxi businesses to enhance the productivity and degree of personalization of their companies.

It is through AI that White Label Taxi Dispatch app such as Uber and Lyft decide the price of your ride and minimize the waiting time after a cab has been booked. And in case you were querying how Uber sends you a discount message just around the time you usually hire a taxi, you now know the answer.

Apart from these existing applications of AI in the taxi business, the recent development of an AI-based Taxi Booking and Dispatch System has got the whole on-demand taxi industry humming. This system guarantees to automate and improve taxi app functions and processes to keep the app in line with the demands of both the passengers and drivers. It helps you make your taxi app up-to-date with the current technological standards and would most likely keep it consistent in the future transportation market.

Advantages of AI Driven On-Demand Services:

Integration of AI in the processes of on-demand services brings a fair share of profits to the service providers.

1. Allow Making Decisions Smartly:

Data-driven analytics help business operators make smarter and well-informed decisions. Through a quick, accurate and in-depth analysis of historical patterns concerning consumer behavior and demand for services, AI technology allows business operators to find historical trends while taking a business decision. Predictive Analysis also helps you provide recommendations to your users, which is a significant revenue stream for on-demand service apps.

2. Customized App For Improved User Experience:

The degree of personalization is what that separates an ordinary on-demand service app from a highly successful one. Your app users like to receive information, content and special offers that are specifically designed for them.

A significant benefit of integrating AI in your mobile app is that you get complete access to the user location, their peak activity time and platforms they often visit. You can then design special offers for them, at a time when they are most active and increase user interaction with the app. AI lets you take personalization to a level that was previously unattainable.

3. Analyze and Interpret App Sentiments:

Be it on social media, forums or across messaging platforms; you get an insight about how your users interact with your app and a load of valuable user feedback from external platforms. This data is invaluable; mainly when you are reviewing your app features, launching a new service or modifying user policy.

Cost to build Taxi App Like Uber

Advantages of AI Taxi Dispatch System:

The benefits of an AI-based Cab management software are numerous for all the business persons, the passengers, and the drivers. The most notable ones include the following:

1. Demand Forecast:

Data-driven analysis and Predictive Analysis together increase the accuracy levels of demand prediction. AI’s effectiveness in Data mining is employed to recover historical data concerning the relationship between demand for taxis and time. The data is then analyzed using Predictive Analysis to provide highly accurate demand predictions. It helps the business operator mobilize resources efficiently.

2. Route Optimization:

Logical Reasoning, a key component of AI, could be used to optimize drivers’ routes. The reasoning algorithm studies data sourced from the drivers who have traveled on the paths. The information is then analyzed for two factors, time and direction, and then optimizes the routes to assist the drivers to reach their destination in the shortest possible time.

3. Autonomous Dispatch:

Data mining and pattern identification help change and give a structure to chaotic and disorderly data. This function simplifies data analysis which in turn helps solve some of the most relevant problems disturbing the dispatch system. Human error, a common question that arises in a taxi dispatch system, could be eliminated by including AI in the taxi business.

A case study of Japan four-way collaboration with the objective of combining AI in the taxi business

Toyota, JapanTaxi, KDDI Corporation, and Accenture have together formed a four-way collaboration in Japan with the objective of combining AI in the taxi business.

The investment has resulted in the successful development of a taxi dispatch support system which foretells taxi service demand. The system connects data sourced from taxi service records with demographic forecasts made by location-based data along with other factors affecting taxi demand including weather conditions, availability of public transport, etc. The system utilizes AI for foretelling the number of occupied taxis in Tokyo. Recent trials have proposed a 94.1% certainty in predictions.

Each company performs a specific role. Toyota collects and examines all relevant data and provides taxi demand prediction data to JapanTaxi. JapanTaxi develops the app which contains this data to the drivers. It also collects information concerning the location of unoccupied taxis, service logs, and the suitable routes to find a passenger.

KDDI generates a demographic prediction technology that analyzes and studies people’s movements from KDDI’s location-based big data. Accenture is tasked with developing the AI’s analytics algorithm that powers the demand prediction engine. This synergy of resources and knowledge has created a mostly accurate taxi dispatch support system by implementing AI in the taxi business.

With an AI-based taxi dispatch support system, the ease of making accurate forecasts for your taxi business is increased exponentially. AI and Machine Learning are about to become essential components of a taxi app business. The earlier you adopt and integrate them into your business, the higher the advantage you get over your competitors.

We provide white label cab dispatch system for all types of taxi businesses all over the world. Our available system can be customized according to your business needs. If you want to make your business successful by using advanced AI system for your transportation business, contact us today.

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