With the global focus on optimizing commuters’ safety, there is high demand for technologies providing 360-degree view of surrounding vehicles to reduce accidents. An autonomous vehicle is poised to witness adoption of breakthrough sensing solutions such as solid-state LiDARs for depth sensing and Blockchain for data security.
Autonomous vehicles can disrupt existing ownership and business model for OEMs as well as other stakeholders. Developments in sensing technologies such as solid-state LiDARs and sensor fusion are a major driver for advancing autonomous driving. Legal concerns and data security issues are hindering wide-scale adoption of autonomous vehicles. Governments globally are focused on developing robust frameworks for autonomous vehicles.
The technology and innovation report highlights some of the key emerging application scenarios of autonomous vehicles in the near term. This report discusses various enabling technologies and application scenarios of fully-autonomous vehicles.
Some of the key questions addressed in the report are highlighted below:
1) What is the impact of fully autonomous vehicles?
2) What are the key drivers and challenges of autonomous vehicles?
3) What are the key technologies enabling fully autonomous vehicles?
4) What are the applications of autonomous vehicles in the near term?
5) Which are key patent areas and companies providing technology solutions for autonomous vehicles?
6) What sort of strategies OEMs need to embrace to gain competitive advantage?
Table of Contents
1.2 Research Methodology
1.3 Research Methodology Explained
1.4 Summary of Key Findings
2.2 Gradual Advancements in Levels of Automation Will Enable Fully Autonomous Vehicles by 2025
2.3 Government Regulations and Policies Hinder Wide-Scale Adoption of Fully Autonomous Vehicles in the Near Term
3.2 Sensor Fusion - A Key Technology to Synthesize Sensing Data from Multiple Sensors
3.3 Solid-State LiDARs to Overcome Packaging and Cost Constraints of Conventional LiDARs in the Long Term
3.4 Key Sensor Fusion Patents for Autonomous Vehicles
3.5 Company to Action – ADAS Systems - Bosch
3.6 Blockchain-based Platforms to Play a Crucial Role in Safeguarding Data Across Autonomous Vehicle Ecosystem
3.7 Deep Learning Networks and Quantum Cryptography Integrated with Blockchain to Enhance Security of Autonomous Vehicles
3.8 Key Blockchain-Oriented Patents for Autonomous Vehicles
3.9 Company to Action – Data Security – Xain AG
3.10 V2V, V2I, V2N, and V2P Applications Emerging as Core Focus Areas for Autonomous Vehicles
3.11 DSRC and 5G Poised to Emerge as Complimentary Technologies Enhancing Safety of Fully Autonomous Vehicles
4.2 Scenario 1: House-held Vehicles to Provide Data-Monetizing Opportunities
4.3 Scenario 2: Strategic Partnerships Essential to Deploy Efficient Ride-Sharing Platforms for Autonomous Vehicles
4.4 Scenario 3: Strategic Collaborations with Goods Merchants to Drive Pilot Projects for Developing Last-Mile Delivery Solutions Using Autonomous Vehicles
4.5 Scenario 4: Robo-cabs to Disrupt Inside-City Transportation in the Near Term
4.6 Growth Opportunities for Autonomous Vehicles
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