Deep learning holds the license to autonomous driving

A disruptive change 

Autonomous vehicles are a billion-dollar market fuelled by state-of-the-art technologies such as AI and ML.

With the automotive industry transitioning from hardware to software-defined vehicles, the entire automotive ecosystem is experiencing exponential growth. Fully autonomous vehicles no longer seem a far-fetched reality today. The market is witnessing a significant shift with auto giants such as BMW, Mercedes, and Tesla investing in both capital and research effort to establish the necessity and feasibility of fully autonomous vehicles, which can facilitate large-scale commercial production.

Technology upgrade

Invest to achieve autonomy and safe mobility 

Over 70% of the $50 billion investment has been made by investors from outside the automotive industry. These investments have helped drive innovation using technologies like artificial intelligence (AI) and machine learning (ML), and auto components like sensors, high performance computing units and so on. Furthermore, capital investments and considerable advancements are being made towards achieving level 5 or full autonomy.

The semi-autonomous or SAE (Society of Automotive Engineers) defined level 2/level 3 vehicles require partial human intervention, and accidents with pedestrians, cyclists, drivers, or other objects cannot be ruled out. A system that doesn’t act on a predefined set of rules but processes real-time information to make life-saving critical decisions based on AI and ML is the need of the hour. 

More than $50 billion has been invested in the autonomous vehicle segment in the past five years. Over 70% of these investments have been made by investors outside the automotive industry.

A deep learning approach

Deep learning has helped solve complex automotive demands.

Autonomous driving consists of three major components: sensing and perception, path planning, and control. Level 2/3 semi-autonomous vehicles have rule-based control algorithms implemented in conventional controllers. These however cannot support higher autonomy, which requires intelligent planning and control with absolute accuracy. With the availability of high-performance computing (HPC) platforms for training and calibration of deep learning models, autonomous driving software development has shifted towards deep learning-based approaches.  

Deep learning in AI has helped solve complex image processing, speech recognition, and natural language processing requirements. Deep learning can also help in obstacle detection, perception of surroundings, driver behavior monitoring, validation of systems, path planning, sensor fusion, intelligent control of vehicle dynamics, diagnostics, cybersecurity, and a lot more. 

Exploring further

Deep learning has been predominately deployed in sensor processing and perception.

To achieve full autonomy (SAE level 4/level 5), we need to explore deep learning in control systems as well. Deep learning-based control can help manage complex lateral and longitudinal maneuverings. It can effectively calculate steering commands for lateral control and acceleration and braking commands for longitudinal speed control of the vehicle. Even so, the safety of deep neural networks can be unstable under adverse conditions. To ensure safety, the automated control system must be trained using varied data sets.  

Deep learning-based control algorithms, to be fully functional, should be trained on a multitude of diverse datasets including geography, weather, and road conditions such as dynamic road environments, background lighting, position, dimension, color, and shape. As we move towards full autonomy, organizations should also be equipped with highly accurate and efficient platforms for data handling, and AI-based algorithm development and validation.