22 Deep Learning use-cases in Transportation

Table of contents:

• Introduction to Deep Learning

• Deep Learning in Transportation:
  I. Traffic Data Analytics
  II. Autonomous Driving
  III. Assisted Driving
  IV. Transportation Infrastructure

• Conclusion

Deep Learning. The new hot topic in computer science.

Today we talk about Deep Learning, an emerging branch of machine learning that uses Artificial Neural Networks to learn from large amounts of data and perform complex tasks. Artificial neural networks are layers of code that act as data processing units to work like the human brain to reinforce information and learn patterns by mapping important information in the data. Deep learning has been applied to various domains in recent times, such as computer vision, natural language processing, speech recognition, and more.

In this article at OpenGenus, we will explore some of the uses of deep learning in modern day transportation and transport systems, and how it has improved the methods of transport we prefer today.

I. Traffic Data Analytics:

One of the uses of Deep Learning in transportation is to analyse traffic data and provide insights for better traffic management, planning, and control.

• Application Scenarios:

  1. Represent transportation networks as graphs and learn their properties and dynamics.

  2. Forecast traffic flow and congestion based on historical and real-time data.

  3. Control traffic signals to optimize traffic flow and reduce delays.

  4. Detect vehicles and pedestrians in images and videos from cameras and sensors.

  5. Analyse traffic incidents and accidents from data and alert authorities.

  6. Predict travel demand and mode choice based on user preferences and behavior.

• Some common Deep Learning models that enable us to perform Traffic Data Analytics are:

  1. Graph neural networks (GNNs): These are neural networks that can operate on graph-structured data, such as transportation networks, and capture traffic flow properties and dynamics.

  2. Convolutional neural networks (CNNs): These are neural networks that can extract features from images and videos, such as vehicle, pedestrian, and lane detection.

II. Autonomous Driving:

A second use of deep learning in transportation is to enable Autonomous Driving, which is the ability of vehicles to drive themselves without human intervention. Autonomous driving has the potential to revolutionise transportation by reducing human errors, improving safety, saving time, lowering costs, and enhancing mobility.

• Application Scenarios:

  1. Perceive the environment and recognize obstacles, pedestrians, lanes, signs, signals, etc. from sensors such as cameras, lidars, and radars.

  2. Plan the optimal path and trajectory for the vehicle based on the current state and goal.

  3. Control the vehicle's steering, acceleration, braking, etc. based on the planned actions.

  4. Analyse human drivers' behaviors and preferences to complement human drivers and improve auto-driving performance.

• Some common Deep Learning models that integrate Autonomous Driving into modern day vehicles are:

  1. End-to-end neural networks: These are neural networks that can directly map sensor inputs to control outputs, without intermediate steps or modules. This is the model that is used in self-driving cars.

  2. Imitation learning: This is a learning technique that can train neural networks to mimic human drivers’ behaviors and preferences from demonstration data to help build better self-driving models.

III. Assisted Driving:

Assisted Driving is a technology that enhances human drivers’ capabilities and safety by providing various levels of assistance and automation. Assisted Driving can range from simple features such as cruise control, lane keeping, and parking assist, to more advanced features such as adaptive cruise control, traffic jam assist, and highway assist.

• Application Scenarios:

  1. Detect and track the surrounding vehicles, pedestrians, cyclists, etc. and estimate their positions, velocities, and intentions.

  2. Predict the future states and trajectories of the ego-vehicle and other agents based on the current situation and historical data.

  3. Generate warnings and alerts for the driver in case of potential hazards or collisions, and intervene when necessary to avoid accidents.

  4. Learn from human drivers’ feedback and preferences to customize the level of assistance and automation for each driver.

• Some examples of Deep Learning models that are currently being used to integrate Assisted Driving into modern day vehicles are:

  1. Deep neural fields: These are neural networks that can model the spatial dependencies and interactions among multiple agents in the scene. This is the model that is used in traffic jam assist and highway assist.

  2. Facial recognition: These systems that can detect drowsiness based on facial expressions using convolutional neural network models. The system can classify the facial photograph feed into active or drowsy based on the facial features learned by the neural network to detect and avoid accidents and faults on roads.

IV. Transportation Infrastructure:

Another use of deep learning in transportation is to monitor and maintain Transportation Infrastructure, such as roads, bridges, tunnels, railways, etc. Transportation Infrastructure is essential for the smooth functioning of transportation systems, but it is also subject to deterioration, damage, and failure over time. Therefore, it is important to inspect and repair transportation infrastructure regularly to ensure its safety and performance. Deep learning can be used to:

• Application Scenarios:

  1. Detect cracks, potholes, corrosion, etc. in images and videos from drones or satellites.

  2. Estimate the structural health and condition of infrastructure based on sensor data.

  3. Predict the remaining useful life and failure probability of infrastructure components based on historical data.

  4. Optimize the maintenance scheduling and resource allocation based on the predicted condition.

• Some examples of Deep Learning models that are currently being used in Transportation Infrastructure:

  1. Stacked auto-encoders (SAEs): These are neural networks that can learn low-dimensional representations of data using unsupervised learning and reconstruct them using supervised learning.
  2. Long short-term memory (LSTM) networks: These are a type of RNNs that can handle long-term dependencies and avoid the problem of vanishing or exploding gradients.

In conclusion,

Deep learning has enabled various applications that improve transportation in terms of efficiency, safety, sustainability, mobility, adaptability, resilience, durability, and cost-effectiveness. Deep learning is a powerful tool that has helped create a stronger environment in transportation systems and engineering.