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Mobility & Transportation

Machine learning in autonomous driving

December 8, 2021
Machine Learning algorithms collect data from their surroundings through cameras and sensors, interpret that data and make decisions

Autonomous driving refers to a completely self-driven vehicle that is controlled by an automatic driving system and needs no intervention from physical drivers. Classified under multiple levels — from fully human-controlled to semi-autonomous to fully self-driven — autonomous vehicles are currently in varying functional and experimental stages. In self-driven vehicles, machine learning (ML) algorithms collect data from their surroundings through cameras and sensors, interpret that data and make decisions without human intervention.

Machine learning algorithms

  1. Supervised algorithms: Use a training dataset to learn and keep learning until a stage of minimal errors is reached.
  2. Unsupervised algorithms: Learn by trying to decode the available data. They identify patterns within the data or divide the data into subgroups based on the similarities between them.
  3. Reinforcement algorithms: Fall somewhere in between the first two, they analyze possible outcomes, take a call based on the best one, and then learn from it.

Machine learning and ADAS

Although fully autonomous vehicles are yet to hit the roads, semi-autonomous cars with Advanced Drive Assistance Systems (ADAS) are highly dependent on machine learning. The application running an autonomous car’s infotainment system receives information from sensors — GPS, radar, lidar, sonar, etc. — and cameras, identifies the obstacles and predicts the next move. Machine learning algorithms can also monitor driver gestures, speech recognition, and language translation and incorporate them in the car’s system. It primarily performs four tasks in an autonomous vehicle:

  • Object detection
  • Object identification
  • Object localization
  • Movement prediction

Machine learning algorithms for autonomous vehicles

  • Regression algorithm: The relationship between two or more variables is estimated and their effects are compared. A statistical model of the relation between a particular image and the position of a specific object within that image is formed with repetitive aspects of an environment for fast online detection via image sampling.
  • Pattern recognition algorithm or classification: Pattern recognition in a data set is important to classify the objects. Here, data obtained through ADAS are filtered by detecting object edges and fitting line segments and circular arcs to these edges, which are then combined to come up with the final features for recognizing an object.
  • Cluster algorithm: Mostly used in cases of unclear images or if there is difficulty in detecting and locating an object due to low-resolution images or lack of enough data points. Clustering is good at discovering structure from data points in such situations.
  • Decision matrix algorithm: Mainly used for decision making and determining moves of self-driving cars (taking left/right turns or need for brakes), this algorithm identifies, analyses, and rates the performance of relationships between sets of values and information.

Limitations of machine learnings

Despite all the advantages of machine learning algorithms in the autonomous driving ecosystem, it is likely to have some limitations, as well. Some of the loopholes that could come in the way of a flawless machine learning system are:

  • Too much requirement of data
  • Presence of distorted data
  • Need for time and resources to verify the authenticity of the data
  • 100% replacement of human intelligence and instinct

Autonomous vehicles are, in all probability, the future of the automotive sector. Major global automakers and technology companies are working tirelessly to develop fully self-driven and machine learning will have a key role to play in this.

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