With the autonomous vehicle industry racing from zero to warp speed, every aspect of the driving world is set for innovation and transformation, and Artificial Intelligence (AI) development in autonomous driving is to bring that transformation, as it is capable of achieving more than what can be imagined. For situations that require hours of programming for dealing with one particular scenario while driving can now be dealt by a deep neural network, wherein the data scientist just needs to expose the DNN to thousands of images from which it can learn. For true enablement of Level 4 and Level 5 automated driving, the system should be functional in all weather and driving conditions.
Deep learning is expected to be the most adopted approach to develop AI as it learns and starts to think by itself without the need of regular human intervention. This means that the AI will be capable of dealing with the several use cases displaying advanced levels of thinking which is required for autonomous vehicle to function in the real world. This is what is happening in AI development for robotics, which is briskly percolating for AD development. Using deep neural networks, the system can make decisions that provide a clear understanding of the driving scenarios and can make justified decisions when driving in the autonomous mode. Besides safety and autonomous driving, AI would be present in several aspects in the automotive industry such as speech recognition, computer vision, connected cars, and virtual assistants. OEMs in the market would like to partner with skilled startups to develop their capabilities to a broader sense. Advantages of using the AI approach include low lead time for development, ease of testing, addition of a wider range of use cases for autonomous driving, and reduced cost of development as compared to the traditional approach. Object detection, classification, and subsequent learning for decision making based on an internally learnt algorithm to help fasten development.
The industry still remains uncertain of the actual power of AI. Direct access to cars enables hackers to compromise the security of the vehicle and user. Data ownership and usage rights are another key concern for end users. Currently, all data gathered are owned by the OEMs. It is difficult for the programmers to validate what the system has learnt after training. Several simulations are required to assess the software capability. Moreover, the industry today lacks a well-defined framework for use of AI in autonomous driving.
- What are the key pillars of AI? Why does it become so important?
- Who are the major startups leading in the race to successfully apply artificial intelligence techniques to achieve full autonomy?
- Who is the biggest innovator providing an end-to-end solution for AI development?
- Who is currently leading the development of AI for AD?
- How do OEMs in the AI race stack up?
1. Executive Summary
- Key Findings
- Top Trends Driving the Development of AI for AD
- Levels of Automation Defined With Regard to AI
- Expanding Universe of AI in AD—Vital Pillars
- Value Chain Development of AI in Universe of AD
- Noteworthy Companies With AI Capabilities—By Region
- Major Tech Companies’ Approach—Overview
- Adjoining Revenue Opportunities for Artificial Intelligence in AD
- Major Challenges in Implementation of AI in AD
- Key Trends
2. Research Scope and Segmentation
- Research Scope and Segmentation
- Research Scope
- Key Questions This Study will Answer
3. Automated Driving Artificial Intelligence versus Traditional Approach
- Automated Driving Artificial Intelligence versus Traditional Approach
- Traditional Approach Versus Deep Learning Approach
- AI—Key Differentiators
- Dependence of AI Development on Software
- Progression of AI in Autonomous Vehicles
- Disruption in the Automotive Industry with Developing AI
- Role of Data Flow in AI in AD Cars
4. Deep Learning in AI
- Deep Learning in AI
- DNN to Drive Self-learning AI
- Deep Neural Network—Training Cycle
- Challenges for Deep Learning Adoption for AD
- Machine Learning Approach—Case Study: Oxbotica
- Deep Learning Approach—Case Study 1: Drive.ai
- CNN—Case Study: AIMotive
5. Innovation Through Partnerships
- Innovation Through Partnerships
- NVIDIA—A Complete End-to-end AI Solution: Hardware
- NVIDIA—A Complete End-to-end AI solution: DL Software
- NVIDIA’S Activity—Highlighted Partnerships
- Companies Ahead in the Business—Overview
6. Major OEM Activities
- Major OEM Activities
- Major OEMs and AI—How They Rate Against Each Other?
7. Growth Opportunities and Companies to Action
- Growth Opportunities and Companies to Action
- Growth Opportunity—Investments and Partnerships from OEMs/TSPs
- Strategic Imperatives for Success and Growth
8. Conclusions and Future Outlook