Scroll Top

Machine Learning – Powering the Autonomous Driving Assistance System (ADAS)

Autonomous Driving Assistance Systems (ADAS) are elaborate electronic systems that assist drivers, enhance driver, passenger, and road users’ safety, and in some cases, even assume control of the vehicle.

The enabling technology allows computers on-board to observe and perceive the external environment using an array of internally fitted sensors and process the input data. Devices like cameras, ultrasonic sensors, light detection and ranging (LiDAR) systems, and radars all collect extensive data from the vehicle’s surrounding environment.

Not only do these systems provide drivers with 360-degree awareness of their surroundings, but they can also react to events that are outside the driver’s current line of vision. They can anticipate and react sooner than humans would do. Industrial IoT combined with Machine Learning (ML) algorithms run through the maze of these devices enabling successful movements in autonomous cars.

How Machine Learning Fuels Autonomous Cars

Self-driving cars should be smart enough to steer themselves under all conditions, much like human drivers. Sensing poor weather conditions such as heavy rain, dense fogs, and coping with backlights when exiting tunnels should come to them easily. Events of encountering unfamiliar objects on roads, avoiding collisions with them, taking detours due to bad road conditions or closed roads are other situations that ML algorithms must cope with. ADA Systems must work successfully across expressways, arterial, and narrower public roads.

Machine Learning is critical to any ADAS to enable real-time perspective, predictive, and cognitive decision making. ML and Deep Learning (DL) are imperative to most autonomous driving architectures to facilitate tasks such as motion planning, vehicle localization, road-marking detection, traffic sign detection, pedestrian detection, automated parking, and vehicle cyber security.

ML algorithms of Autonomous Driving Assistance Systems initiate predictive and prescriptive analytics and then move on to Deep Learning cognitive actions.

  • Predictive analytics Machine Learning algorithms forecast based on data gathered by models based on questions like what is likely to happen in the future and by learning from previous trends and patterns.
  • Prescriptive analytics ML models help choose the best course of action to bypass or eliminate further issues.
  • Cognitive Analytics is a combination of Artificial Intelligence (AI) technologies like ML and Deep Learning to create algorithms that think like human brains to perform tasks; in this case, initiate the right task according to the vehicle’s external environment. Any ADAS becomes smarter and more effective over time as it learns from its interactions based on these models.

Tasks of ADAS in Self-Driving Cars

Broadly, the tasks in self-driving cars can be divided into 4 sub-tasks

  • Object detection
  • Object identification
  • Object classification, and
  • Object localization and movement prediction

In the eco-system of recognition, prediction, and cognitive action, objects have to be detected for tracking. Tracking in an ADAS refers to detecting moving objects and continuously following their trajectories in every frame. ML algorithms of ADAS must detect, track, and classify objects to avoid obstacles and collisions, and plan paths, for smooth driving. Such algorithm models are capable of super-fast computation of pose variations, partial occlusions, and changes in illumination.

Using discriminative correlation filter bank (DCFB) and optimizing frequency domains help in producing advanced trackers. Object detection using LiDAR, categorizing it into drivable and non-drivable regions can help generate accurate lane mapping for complex urban routes.

Decision-making systems in ADAS comprise a complicated network of ML and DL algorithms that perform data analysis from sensors and other sources along with proprioceptive data from the vehicle.

Best ADAS are those that receive a significant amount of training on large chunks of data based on key parameters, to make the right decisions. The process, of course, entails considerably high investments in terms of time and money.

AWS for Predictive, Prescriptive, And Cognitive Analytics

Using fully managed AI infrastructure like those of AWS can help mitigate the problems encountered in developing quality, economical, and smart systems quickly. The platform enables accurate predictions and deeper insights for better prescriptions for improved ML models.

  • Amazon SageMaker Studio helps in accelerating all ML developments. The platform simplifies ML lifecycles by automating the system and allows companies to build, and train custom-made models at scale. SageMaker MLOps and its fully managed infrastructure, workflows, and tools smoothen the deployment process and management of any Use Case model at scale.
  • Amazon Rekognition Automate Image and Video Analysis employs a highly scalable Deep Learning technology that makes image and video analysis for applications that we build faster and smoother. Its algorithms can identify objects, scenes, people, text, activities in videos and images along with content that may be inappropriate. All these helps companies build solutions that are specific to a range of ADAS and other business needs.

Summing it up

Autonomous Driving Assistance Systems are powered by robust Machine Learning algorithms that connect a maze of devices enabling detection, identification, classification, localization, and movement prediction of objects around it. The system predicts outcomes from what it has learned by training on videos and images, prescribes the best action to avoid any future issues and finally carries out a cognitive action based on the former stages.

Building intricate ML models for the ADAS requires intense training that involves time and money. The quality of AI is critical to the success of any such system. Competency Partners of AWS AI leveraging its infrastructure like Amazon SageMaker, and Amazon Rekognition can help process vast amounts of data, build multiple models concurrently, evaluate models, and deploy end-to-end customized solutions better, and faster. Connect with us to fast-track your initiatives.

Leave a comment