This document discusses key technologies enabling autonomous vehicles, including sensors, vision processing, artificial intelligence, deep learning, and application lifecycle management. Sensors like LiDAR and cameras combined with sensor fusion are used to give vehicles a 3D view of their surroundings. Artificial intelligence and deep learning are important for vehicles to interpret sensor data and make decisions. Application lifecycle management is also important for safely developing and integrating the various complex technologies involved in autonomous driving.

1. Key Technologies for
Autonomous Driving

2. Introduction
• The term ‘Autonomous Driving’ has been in the buzz off late
• It is forecasted that there might be significant disruptions taking place in the
automotive industry
• Emerging automotive providers in India and abroad have partnered with car
manufacturers to penetrate into this growing market
• This partnership has been successful, as we are witnessing self-driving
technology

3. Technologies for Autonomous Vehicles
• Various advanced technologies are contributing towards the development of
Advanced Autonomous Driving & Self-Driving cars
• Some of the notable technologies fuelling the growth of autonomous cars
include-
1. Sensors and Vision Processing
2. Artificial Intelligence and Deep Learning
3. Application Lifecycle Management

4. Sensors
• Automotive companies are now
combining sensor technologies for
enhancing the situational
awareness of autonomous cars
• Self driving cars are using refined
variants of infrared sensors
• LiDAR (Light Detection and Ranging
Technology) sensors are used for
calculating the distance between a
vehicle & its surroundings
• With rotating LiDAR sensors, it is
becoming possible to have a 3-D
view of the vehicle for detecting any
obstacles

5. Sensor Fusion
• Sensor fusion is an emerging
software technology that aims to
synthesize the data collected from
various sensors for providing
accurate position and orientation
data of the vehicle
• It is a critical component of any
autonomous vehicle which operates
on parallel data inputs from
different sensor types
• All these inputs are combined to
give a clear understanding of
vehicle’s surroundings

6. Vision Processing
• LiDAR-based sensors are costly, as a
result some providers are opting
camera-based tools
• These camera tools provide
accurate information of the
vehicle’s surroundings

7. Artificial Intelligence
• The future vehicles ae going hold
vast amount of information related
to their ecosystem
• A small challenge persists regarding
complete automation
• The vehicle must interpret the
incoming data & make decisions
based on the information
• AI will be a major component in the
autonomous technology

8. Deep Learning
• Deep learning uses artificial neural networks for allowing a system to learn
and perform its own decisions
• Multiple sets of inputs and decisions in a driving scenario enables the use of
such learning algorithms
• Self-driving cars will offer better services with image recognition, processing,
and decision-making using deep learning
• The autonomous vehicles will analyze, understand and react to the context in
a better manner when driven in simulated scenarios

9. Application Lifecycle Management (ALM)
Integration
• Development of self-driving and autonomous vehicles is a complex task, as it
involves deploying various advanced technologies
• Integration of major components can become complicated; automotive
technology providers like ARAI closely monitor the current and future
regulations related to self-driving
• ALM play a major role in managing complex development procedures of
autonomous cars through compliances and standards
• Such platforms offer support for multiple engineering processes and assure
transparency with traceability required for safer delivery of self-driving cars

10. Know more about ARAI, the leading automotive R&D
organization in India, by visiting
https://technovuus.araiindia.com/home/aboutUs