AI In Transportation Industry – Use Cases and Applications in 2021

Ai in transportation is scaling at a different height each day. The above video is by AutoX, they have started their fully driverless RoboTaxi pilot program for people in Shenzhen, China. This reminds me of the scene in Hollywood. You enter the car and there is a ghost driver! 

Although that ghost driver is invisible but smart, swift, and very punctual. This is one of its kind where people can book a completely autonomous RoboTaxi without any safety drivers on board. 

The RoboTaxi here is really impressive, it’s running on regular roads, takes unprotected turns, makes side passes, deals with a scooter running traffic lights and so much more. 

How can ai-powered autonomous transport vehicles understand what’s around them?

How is all of this technically possible? Well, these cars are embedded with hi-tech sensors, each sensor identifies objects on the way, and with the help of highly accurate data annotation techniques sensors can tag those objects. 

For example: Like the car here identified a scooter running traffic lights, this could happen only because the sensors could understand that there was an object. That object was tagged as a scooter with the help of data annotation techniques. This makes machine’s understanding capability as simple as humans. 

Check out this interesting talk by Chris Urmson, head of Google’s driverless car program, one of the several efforts to remove humans from drivers’ seats. He also shares fascinating footage of how cars see the road. 

Ai in Transportation, a brief overview

There are a lot of questions running in our minds like, how will the transportation system of the future look like? Are driverless cars going to become a new norm? Will we have robots delivering products and food to our homes directly or are there going to be drones flying in the sky and dropping packages? 

Flying cars maybe? Ok, let’s now get too futuristic here – but let’s face the fact that the transportation landscape is changing and transforming like never before. AI will create a sci-fi real, sophisticated and fast way of getting from point A to point B safely and cheaply. 

Seems like a dream but technologies like computer vision and deep learning are bound to make this a reality. The key is to feed the right data in, if machines can be fed with accurate data then they can see and hear like humans and accordingly respond. Data annotation is the secret weapon that powers the next-gen transformations that are made with AI. 

Now let’s jump-in to the use cases and explore the revolutions in transportation powered by AI.

AI Use Cases in Transportation Industry:

  1. Smart Traffic Management
  2. Passenger Delay Predictions
  3. Drone Taxis
  4. Autonomous Vehicles

AI-Powered Smart Traffic Management 

Technology that can put an end to traffic jams – delightful isn’t it? In 2015 there were 1.3 billion motor vehicles on the world’s roads and with growing affluence in developing economies that number is expected to rise over 2 billion by 2040.

In Germany AI is being used to optimize traffic light control and decrease wait time at intersections. Simulations suggest AI can decrease waiting time by up to 47% as compared to pre-timed signals. 

AI does not only benefit motorists but also bike mobility companies. Siemens Mobility operates a fleet of 1400 electric bikes in Lisbon, Portugal. They use Computer Vision techniques to monitor the availability of bikes and spaces in charging docks for those returning the bikes. 

The predictions are used along with recent traffic information to help bike collection teams restock docking stations and provide optimal routing for service technicians who maintain bikes. 

So, when you are moving around in Lisbon you can ensure that there will always be an e-bike available for you at the station!

Data Annotation for Smart Traffic Management

Texas Advanced Computing Center and University of Texas Center for Transportation Research and City of Austin are developing tools that will enable sophisticated traffic analysis using deep learning techniques coupled with data mining. 

The raw traffic camera footage will be captured and the machine will identify people, cars, motorcycles, traffic lights, and more using data annotation techniques. Accurate, high-quality, real-time data annotation is needed for machine learning models to give real-time inputs to the traffic management and other stakeholders to ensure smooth functioning of the traffic system in the country. 

“We are hoping to develop a flexible and efficient system to aid traffic researchers and decision-makers for dynamic, real-life analysis needs,” said Weijia Xu, a research scientist who leads the Data Mining & Statistics Group at TACC. 

AI-Powered Passenger Delay Predictions

Flight delays are always troublesome. The US incurred a recurrent cost of 39 billion dollars due to flight delays according to a report from the University of California. Flight losses also impact customer satisfaction and experience. 

Customer churn rate has significantly increased because of this and that’s why AI is here to help and eliminate this issue. Leveraging computer vision technology can cut down passengers’ wait time. Models can compile information from weather and combine it with patterns of flight technical glitches and help passengers get rid of unnecessary waiting times. 

The information retrieved can be shared with passengers traveling and help them save unnecessary time spent at the airports. 

Accurate Data Annotation for Computer Vision Models Predicting Passenger Details

Deep Neural Network has proven to be highly effective in predicting flight delay prediction. However this model has drawbacks of overfitting, but researchers have solved that through typical data dropout technique for each step of the repeated training procedure. 

Training data used in building the model needs to be annotated before it is fed into the system. Weather data, customer flight data, traffic data and airport congestion data put together needs to be annotated for the machine to understand and learn. 

Smart data annotation techniques are much in demand in the sector and companies like Labellerr are fulfilling the data annotation requirements in the field of transportation sector. 

AI for Drone Taxis

Drones and Robotaxis enable faster transportation of people and goods. Zipline, a U.S based startup is using drones to deliver COVID-19 test samples in remote locations in Africa. Chile has launched a pilot drone program to deliver medications to people living in remote rural areas. 

The aviation industry has frozen because of the current pandemic situation and hence the use of autonomous flying drones has emerged as a boon in the current situation to beat the crisis. 

Drone delivery of parcels costs 70% less than van delivery. Amazon’s parcel delivery service will be launched all across the world, check out Amazon’s plan:

Data Annotation for Drones

Multiple image annotation techniques are used to create training data for drones. Bounding boxes is one of the major annotation techniques adopted. Drones capture objects in a rectangular or square shape to provide the drone a visual recognition of objects from the aerial position. 

Labellerr’s automated machine learning annotation platform does the job in the shortest and the fastest manner. Here is a glimpse of how you can annotate data using Labellerr’s platform. 

AI Powered Autonomous Vehicles

Check out this cool autonomous bus by Olli, it’s a cognitive electric shuttle that can perform functions like transportations of passengers to request locations, providing suggestions on local sights, and just makes the passenger feel like they are traveling with a human driver.

These kinds of bus trials have been started all over the world. The global non-uniformity in cities infrastructures, road surfaces, traffic, etc have AI predictions Autonomous. 

Autonomous vehicles are our future and it will soon become the new normal.

Data Annotation for Autonomous Vehicles

Surroundings need to be perfectly mastered by autonomous vehicles to reach from point A to point B. Sensors in the car do the job of sensing the objects or people around the vehicle. The vehicle is also installed with LIDAR (Light Detection and Ranging) system and per second data frames may range around 240,000,000 assuming each frame has 20 objects then we will end up with over 4 Billion objects and all of these objects must be annotated. 

Manual annotation is out of the question. Automation is much sought after to annotate every frame with precision and accuracy. Labellerr is a data-annotation platform that provides simple, clear, and easy to use UI with seamless UX to perform boundary box and text classification, entity recognition, etc. annotation on different types of unstructured/ semi-structured data catering to a wide array of industries, Retail, Health care, E-commerce, Hospitality, Businesses to name a few.

Annotate Your Training Data with Labellerr

Experience the truly automated machine learning experience with Labellerr’s complete ML suite. Just plug in your data via a range of connectors as FTP, Local storage, Google Drive, AWS S3, Azure Blob, etc. 

Allow our inbuilt Auto ML feature to suggest annotations based on your requirement. Leverage the Auto-label feature to annotate your data with 10x speed and save crucial man-hours. Get a list of confidence scores of the assigned labels and verify only those with a low score.

Connect Labellerr to the Cloud-Based Compute service of your choice with assured data privacy and train your machine learning models on the go. Without the hustle of downloading the data, Service-specific data conversion formats, Data Leakage to name a few.

Labellerr’s community service initiative

As a part of our community service initiative, we have created a GitHub repository, wherein we list the implementations and walkthrough guides of tools, technologies, state of the art algorithms catering to the latest developments in the field of Deep Learning and do our bit in building a strong community of deep learning enthusiasts.

Head over to our blog where we regularly write about the industrial and corporate use-cases of Deep Learning. The recent advancements in the field and how the current industry is accepting them, building over them in pursuit of solutions that were once deemed unachievable.

Connect with us

If interested, you can get your hands dirty with our precoded notebooks as part of our community service initiative.

Visit our website and mention your use case in brief and our customer engineer will contact you and help you prepare the plan and get you running on a trial with us to validate.

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