AI in Transportation

AI in Transportation All Set to Redefine Urban Mobility

The hypotheses and developments in artificial intelligence stirred all kinds of hypes around this field of study. Research is underway for decades but it was only in the recent years that developments managed to hit consumer markets with integration in a range of products and services. AI in transportation is making a huge impact as computer vision software systems and machine learning algorithms are entirely redefining urban mobility.

Many new concepts would make to the development phase while a number of extensively tested features would be available for commercialization. This articles exclusively states the AI innovations that have the potential to revolutionize the industry in 2020.

Use of AI in Transportation for Autonomous Vehicles

Autopilot in aerial and marine vehicles is around for over a century now. Initially, the technology assisted human pilots in a very limited selection of tasks but improved over the time. We have witnessed a number of entirely autonomous aircraft models popularly known as unmanned aerial vehicle (UAV).

However, it was not until early 1990s that we started having autonomy in vehicles – that too on an intensely limited scale. Mitsubishi is arguably regarded as the first manufacturer to introduce and commercialize cruise control in vehicles. Mercedes-Benz is also one of the earliest automobile makers to invest in autonomy.

Society of Automobile Engineers (SAE) introduced a standard to classify the autonomy levels in commercial automobiles. Cruise control – the ability of a vehicle to maintain its lane and issue warning when a nearby vehicle or another object approaches dangerously near – defines the level 1 of autonomy. The standard also defines level 0 where vehicles are only capable of issuing warning and have no other control whatsoever.

The subsequent three levels further classify AI in transportation. Level 2 vehicles are capable of switching lanes and tackling nearby objects without human assistance but driver is required to keep hands on the wheel at all times. Level 3 allows hands off the vehicle but signals to intervene as needed. At level 5, a vehicle is fully autonomous eliminating human driver’s assistance.

SAE Autonomy Levels

Where does the autonomy in transportation stand today?

The challenges in autonomy are more of a regulatory framework than technical limitations. A number of manufacturers including BMW, Nissan, Tesla, Alphabet, Mercedes-Benz among others claim to have developed sufficient technology for self-driving cars. However, each of them is testing the vehicles under a wide range of circumstances. Besides, they are also ensuring to test the vehicles for a predetermined number of miles to achieve more certainty.

Waymo – a subsidiary of Google’s parent Alphabet – is offering level 4 autonomous taxi rides in Arizona. Tesla aims to introduce fully autonomous vehicles by the end of 2020 indicating a giant leap for artificial intelligence in transportation industry. Uber is testing level 3 and level 4 autonomous vehicles manufactured by Volvo. The ridesharing giant in partnership with some automobile manufacturers is aiming to introduce its flagship self-driving car in 2020.

Learn about some of the case studies at Mob Inspire

The year ahead is set to yield the outcome of the efforts building up for the past couple of decades regarding the association of AI and transportation. Although the large-scale commercialization of self-driving cars is still unlikely, yet technology would see widespread acceptance in various parts of the world.

Mobility as a Service (MaaS) to Enhance Ride-hailing Operations

Mobility as a Service

On-demand taxi service providers have redefined the ride-hailing industry by enabling passengers to instantly book a ride with only a few taps on smartphones. Although radio taxi services prior to the advent of on-demand economy were also providing instant booking over phone calls, yet the on-demand model carries its significance in that it allowed passengers and administrators to track ride in real time.

Besides, customers have the power to pay e-wallet and share feedback as soon as ride ends. Similarly, the apps on driver’s end provides them with the best route to destination. Many ride-hailing businesses allow drivers to find the nearby drivers from same service so that they may relocate to a nearby location where they have greater chances of getting a ride request.

Also read: Use Cases of IoT in Logistics

Ride-hailing is not immune of challenges though. Use of AI in transportation is on its way to address those challenges. A number of problems some of which are stated below have emerged in the decade down the road since the start of Uber era.

  1. Number of vehicles and gig workers registered with ride-hailing businesses is growing rapidly.
  2. Number of privately owned vehicles is not reducing as previously aimed.
  3. Service providers are ending up increasing fares to make their businesses sustainable.
  4. The amount of traffic congestion is increasing with dozens of ride-hailing businesses operating.
  5. Bus-pooling is an alternative but most passengers have to walk excessively to hop on a bus.
  6. Ridesharing or bus-pooling also takes more time than on-demand taxi services.
  7. There are very sharp tradeoffs when deciding between on-demand taxi and ridesharing.
How MaaS tackles the challenges of ridesharing with application of AI in transportation

MaaS also referred to as shared mobility network is a concept that has been widely regarded to define the future of AI in transportation. Instead of providing separate services for on-demand taxi and ridesharing, MaaS providers aim to combine these services so that passengers have more seamless experience.

For instance, a passenger looking to travel from point A to B would be offered the most optimized options that would include on-demand taxi, rideshare, and micromobility services. The resultant ride plan would reduce the walking time to almost minimal and may include one or more types of vehicles. A passenger may get a set of options like the one below.

MaaS AI in Transportation

It is notable that passengers would not require to book each ride separately. Instead, the artificial intelligence in transport system would enable passengers to get the entire ride plan by scheduling only once.

Also read: Technology in Logistics 2020

The transport departments in many developed countries are facilitating the efforts to phase out private vehicles, standalone services for on-demand taxi, public transit and micromobility and replace them with shared mobility. The regulatory framework for this transforming is under development stages for the past few years. It is highly likely that MaaS services would attract a large-scale adoption in 2020 in various metropolises.

Facial Recognition for Gig Driver Verification

Facial Recognition Software AI in Transportation

Uber recently faced a ban from operating in London – one of its biggest markets – on grounds of failure to prevent unregistered drivers from using accounts of other drivers. Uber, Lyft and other major ride-hailing businesses previously introduced a number of measures when the city’s transport authority warned ride-hailing giant of a potential ban in case of failure to meet the standards.

The measures include:

  1. Snap selfies at various times during the course of operations to ensure that the driver is a verified one.
  2. Panic button that allows passengers to call 911 with only a couple of clicks.
  3. Sharing ride-location in real-time with one of the contacts.
  4. Reminders to confirm number plate and driver as the one appearing in app.
  5. Automatic alert system to alarm passenger if ride goes excessively off track.
  6. Extensive background checks and driver education before registering them.

Despite all these measures, the ban of Uber indicates that transport authorities are all set to come down hard on ride-hailing service providers. Artificial intelligence solutions in transportation are enabling ride-hailing startups to overcome this challenge. All ride-hailing businesses will aim to add facial recognition feature to verify driver by at most end of the Q2 in 2020.

Mob Inspire is one of the pioneers in commercial development of facial recognition for gig drivers thereby expanding use of AI in transportation. One of our clients in Australia recently requested this feature. We developed and delivered a comprehensive system that recognizes drivers based on Computer Vision technology.

AI for Vehicle Surveillance and Traffic Forecast

Vehicle surveillance with AI in Transportation

Surveillance cameras are being used in hundreds of cities exclusively for surveillance of vehicles. The authorities retrieve the pictures from a particular time of the day to identify crime suspects.

Computer Vision technology is an impressive application of AI in transportation that identifies license plate in a picture to allow characters reading. Mob Inspire uses a combination of Computer Vision algorithms along with image processing to find a license plate in an image that may have any number of objects apart from vehicles.

Once the license plat is found, the algorithms of optical character recognition (OCR) are used to read the characters on plate. The outcome is compared with the database of vehicle registration departments to determine the origin and owner of vehicle. This way, authorities keep a track of each vehicle that passes through any junction in the city.

Artificial intelligence in transportation industry is also enabling transport departments to craft effective projections for traffic. Intelligent systems can identify growing traffic at one road and reducing at another in real-time. They use this information to attach bias in traffic signals so that each driver may get more or less equal wait time.

The use of big data with AI augments the capabilities of intelligent software systems. Traffic management units perform predictive analytics to identify the capacities of roads at usual times and during blockades. The proposed shared mobility network would also integrate passengers by sending them real-time notifications on traffic and recommendations for their route plan.

Drone delivery and AI powered network control system

Drone Delivery Network

Ground based on-demand delivery has been a huge source of relief to consumers’ lives who can deliver and receive parcels at their doorstep within an hour of placing the order. However, the excess of delivery businesses is going to make the space saturated in future. We already witness some cities reeling from traffic congestion.

Research in the past couple of decades indicates that mini drones are highly effective for delivery in urban areas due to growing congestion. Some drone delivery startups have emerged over the past few years mainly in San Francisco and Los Angeles. The operations from these businesses are so far so good.

However, there would be challenges in future as the number of drone traffic would increase. In a conventional case, the administrators will have to define speed, height and path for each drone in a spatial region.

Also read: Impact of Blockchain in Transportation

This is where Artificial intelligence solutions would be vital. Drone based delivery would serve little purpose if operators have to control each drone manually. AI enables you to make the entire delivery process automated from the time a drone is loaded with a parcel, delivered to destination, and returns. This is one of the most notable cases of future of AI in transportation.

The developers at Mob Inspire take their inspiration for drone delivery network from air traffic control systems. The difference lies in the fact that airplane pilots and UAVs set their path as per the directions from ground-based controller whereas drones in an AI network make the route plan independently unless explicitly tasked. A drone can set the height, speed and all notable parameters itself by identifying all other drones in the network.

Drones based on-demand delivery services are emerging for the past few years and 2020 would witness their expansion on a significantly large scale. The year would also possibly bring a regulatory framework for this industry.

Prepare to Live Ahead

The industries associated to transportation including logistics, delivery, ridesharing and automobile are all set to incorporate large-scale changes. Businesses are testing innovative features to enhance operations and optimize costs for years. The year 2020 will transform these industries with the innovations shared above as administrations are finalizing the corresponding regulatory framework.

It is about time you plan ahead of competitors and become of the pioneers in providing these features of AI in transportation. This investment is not only going to facilitate your customers but enable you to minimize costs and time consumption significantly.

Mob Inspire carries an experience spanning over a decade in developing intelligent systems. Our widespread clientele leads their respective industries by utilizing our transportation solutions.

What are you planning to accomplish? Do you intend to optimize your business with matchless software infrastructure or redefine business model right from scratch? Mob inspire can assist you in crafting a business model and developing a highly efficient software solution customized to your business needs. Contact us today so that our experts can take you further.

Technology in Logistics

Gear up for these technologies in Logistics in 2020

Logistics industry is technology-intensive. We get to see a range of emerging technologies under research and development phases every year and another set of technologies making their ways toward commercial availability. A slight retrospection of the decade reveals that a number of new areas for research emerged while others moved onto the commercialization phase. In essence, technology in logistics is constantly evolving.

This article shares some of the innovative technologies that are highly likely to make commercial availability on a limited or large scale in the logistics industry. Each of these technologies mainly falls in internet of things (IoT), extended reality, automation, blockchain and self-driving domains.

5G integration into IoT for Wearable Technology in Logistics

The cases of IoT in logistics and supply chain are now frequently coming up. Fleet managers can track the locations of each vehicle in real-time while warehouse managers can verify the authenticity of goods. Besides warehouse staff can also locate every item instantly and determine ahead of time when the supply of a product falls short or exceeds the required limit.

The upcoming year would further intensify the use of IoT since 5G is most likely to get commercialized. The speed of data transfer between transmitters and receivers would increase manifold while latency would reduce remarkably.

Improvement in speed and latency is inevitable now because real-time data information sharing is no longer a choice. Instead it is a compulsion for a range of reasons. The two most pressing reasons include:

  1. The US alone suffers from as much as $43 billion annually in food losses. There is an increased demand from regulators as well as retailers and consumers to place perishable items under real-time surveillance.
  2. Apart from food losses, supply chain leaves a huge carbon footprint. As the governments are pushing businesses to implement strict policy for reducing emissions, the businesses have no or little choice except complying with standards.

The number of logistics operators integrating IoT in fleet management systems is going to increase significantly in 2020. Businesses would be required to integrate sensors in fleet that monitor humidity, pressure and temperature and share the results with fleet managers in real-time.

Besides, the use of sensors identifying amount of emissions and the corresponding data sharing medium within a logistics management software would also witness a sharp hike. Each operator would eventually require publicly sharing data about emissions to democratize the entire logistics chain.

Robotic Process Automation for warehouse optimization

The use of QR codes and subsequently, RFID tags enabled warehouse managers to do away with the task of verifying each item manually. QR readers are extensively used in at warehouses and retail stores to confirm authenticity of a product.

Besides, QR readers also enable staff to find price of an item instantly. Scanning a unit having QR code with QR reader shares the results on a dedicated mobile app. This is helpful in multiple ways

  • A store owner can remotely confirm if a product is available in sufficient quantity or not.
  • The managers can tackle counterfeiting of products. Moreover, smart warehouse technology in logistics management also prevent expired or smuggled product units.
  • Staff members of a store can locate a unit instantly.
  • Warehouse managers can perform effective aggregate planning by ensuring that supply corresponds to demand.

Mob Inspire delivers field service management software customized to each operator’s needs. The software solution with its extensive range of robotic process automation tools eliminates the need to perform manual warehouse management. You can keep as little workforce as you wish by adding the amount of automation.

Blockchain Technology in Logistics for instant payment verification

The amount of time consumed in delivering a shipment from one part of the world to another is unnecessarily huge. Each shipment has to be verified for smuggling, payment verification, and counterfeiting by a number of stakeholders including manufacturers, warehouse owners, retailers, end consumers and regulators. Consequently, the cost of logistics also increases significantly apart from time.

Blockchain is widely regarded as the most viable solution for 2020 and beyond. All major fleet operators including MAERSK with its largest containership fleet are adopting blockchain. The effectiveness is not limited to fleet management. Blockchain based logistics management software is also causing a revolution in retail store management.

Two of the most noteworthy use cases of blockchain technology in logistics management include:

  1. Card-less Payment and Verification
  2. Payment through cash was already a thing of past. Blockchain enables you to move even a step ahead to eliminate centralized banking entirely. The decentralized banking through cryptocurrencies allows you to make payments through bitcoins. You can even have your own cryptocurrency if you intend so.

    Apart from payments, logistics operators are successfully verifying payments through blockchain in a remarkably lesser time. The verification that took days in the past takes only a few seconds now. Besides, blockchain enables you to democratize transactions by allowing each stakeholder to have visibility of payment history in real-time.

  3. Retrospective Tracking of Goods
  4. Blockchain provides another strategy to track perishable goods. Each stakeholder can find the date of production and duration of travel for the unit being shipped. The involved parties have the access to entire history but none of them can forge or delete data since blockchain technology is immutable.

    The tracking through blockchain is highly efficient in tackling the counterfeiting of drugs – one of the biggest challenges in global pharma industry today. Each block provides exact and immutable details of the origin of a drug to ensure that retailers do not accept consignment with counterfeited units.

    The accumulative losses from delays in transportation, drug counterfeiting and foodborne diseases are of the order of several hundred billion dollars. Thus, blockchain based logistics management app solutions are inevitable for transportation and logistics industry.

The commercial framework for autonomous fleet in supply chain

Autonomous vehicles are rapidly becoming a norm. The year 2019 remained revolutionary with multiple operators launching level-3 self-driving vehicles. Apart from Tesla for private use, Uber and Waymo are utilizing autonomous vehicles for ridesharing purposes.

Although there are incidents involving crashes, yet the probability of an autonomous vehicle crash during a journey is very small relative to conventional vehicles. The number of miles traveled against that of crashes confirm this claim.

The encouraging results are pushing logistics fleet owners to introduce autonomous vehicles in supply chain too. Some of the sea-based and aerial logistics operator already have autonomous systems. However, a number of operators would introduce ground based autonomous fleet.

“Automated or self-driving vehicles are about to change the way we travel and connect with one another.”

Elaine Chao – U.S. Secretary of Transportation

Some large-scale logistics business managers are partially operating autopilot in vehicles. The autopilot technology in logistics is extensively used during intercity travels where traffic is minimal. The vehicles in the fleet are capable of applying brakes as soon as the leading vehicle applies brake.

This strategy is proving to be paying so well to research and development in fleet efficiency. The vehicles traveling at constant speed with calculated inter-vehicle distance ensure minimal air friction. Consequently, there is a considerable reduction in fuel consumption.

The commercialization of level-5 autonomy is highly unlikely to be on the cards anywhere before 2025, a number of businesses however would be introducing partial autonomy by the second half of 2020.

Extended reality to facilitate drivers and augment driving experience

Some of the major logistics service providers and vehicle manufacturers are introducing augmented reality features in vehicles for a highly immersive experience. The information including but not limited to weather update, cabin temperature, driving speeds, usual and unexpected blockades is now ubiquitous.

The fleet operators can also mark milestones virtually so that drivers would be able to see each labeled object and the corresponding information in real-time. Moreover, you may also provide real-time 3D navigation facility to drivers so that they find and traverse the most optimized route.

Google announced AR Maps earlier this year and launched what it calls Live View. This feature in Google Maps, still in its testing phases, is set to make widespread adoption in 2020.

Apart from enhancing driving experience, augmented reality technology is also enabling truck drivers to repair minor faults. The combination of wearable gadgets mainly AR glasses enables drivers to find the fault lines and take corrective measures.

Many logistics operators are starting to offer a selection of these AR features. DHL shares some of the use cases of augmented reality and wearable technology in logistics in this brief video.

Transportation industry has over 13.3 million employees in the US due to the repetitive jobs. Utilizing extended reality technologies effectively can save a huge volume of costs. Some glimpses from DHL indicate that businesses employing AR would make huge gains in 2020.

Logistics in 2020 – A Recap

Logistics in 2020

Do you look forward to 2020 and beyond?

Logistics industry continues to evolve with innovative technologies. While AI, big data and IoT technologies are around for some years now, many new inventions and advancements including blockchain and 5G are setting feet deeper into delivery, logistics, and transportation.

Mob Inspire stands among the top technology solution providers and logistics app developers in the world with a range of digital solutions including field service management software, last-mile delivery solution, and logistics management.

Do you intend to optimize your logistics business or aim to launch one from scratch? Contact us today so that we can develop a feasibility study and provide you with a comprehensive business plan before moving on to development phase.