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NA and EU Sensor Data Fusion Strategies for ADAS and AD Systems, Forecast to 2025

  • Report

  • 66 Pages
  • December 2019
  • Region: Europe
  • Frost & Sullivan
  • ID: 4895801

Strategies Influencing Key Design Elements of E/E Architecture and Sourcing AD Software to Achieve L3 and Above Autonomy

The evolution in autonomy and increase in the adoption of driver assistance systems have generated a need to sense and perceive the surrounding environment of a vehicle entirely and accurately. The fusion of forward-looking sensor data has given OEMs and Tier-I suppliers opportunities to offer multiple ADAS applications at low costs, while providing high redundancy and reliability in perceiving vehicle surroundings.

The use of multiple sensors and sensor data fusion and an increase in the number of ADAS applications have elevated the amount of in-vehicle data exchange to a few gigabytes, which is expected to rise further, as the level of vehicle autonomy goes up. This will generate the need to increase the speed of data transfers within the vehicle communication network and the use of high-powered control and processing units, which will, in turn, increase the complexity of the vehicle E/E architecture.

The evolution of Connected Autonomous Shared and Electric (CASE) in today’s vehicles has urged OEMs to redesign product development ground up. OEMs are developing new vehicle platforms or changing the design of the existing ones, thereby altering the internal wiring and communication protocols to accommodate electric powertrains, connectivity features, and autonomous applications, including the embedded and decision-making software in existing and future vehicles.

The study focuses on the sensor data fusion strategies for ADAS and AD systems in the NA and EU regions, with forecasts running up until 2025. This report has highlighted the key strategies of sensor data fusion and its influence on the key design elements of E/E architecture and sourcing AD software to achieve L3 and above autonomy. The study discusses the major trends observed in the market and explains the impact scenarios, along with use cases.

Key Issues Addressed

  • What is the need for sensor data fusion?
  • What are the different types of sensor data fusion strategies in the market?
  • What is the optimum sensor data fusion strategy for various vehicle segments?
  • What are the key OEM strategies influenced by sensor data fusion to accelerate the development of autonomous driving?
  • What does the autonomous vehicle value chain look like?
  • How are the key sensors in autonomous vehicles evolving and what are their capabilities?
  • How are the sensor requirements changing with the level of autonomy?
  • How is the vehicle E/E architecture evolving and what are the key topologies?
  • What are the business models adopted by the autonomous software developers?

Table of Contents

1. Executive Summary
  • Key Questions this Study will Answer
  • Sensor Data Fusion Strategies
  • Influence of Sensor Data Fusion Strategies on Vehicle Technologies
  • Vehicle E/E Architecture Strategies
  • AD Sourcing Software Strategies
  • Highlights

2. Research Scope, Aims, and Objective
  • Research Scope
  • Research Aims and Objectives

3. Sensor Suite Evolution
  • Technology Evolution - Radar Module
  • Technology Evolution - LiDAR Module
  • Technology Evolution - Forward Camera Module
  • Rise in Sensor Requirements with Levels of Autonomy
  • Individual Sensor Performance Capabilities
  • Need for Sensor Fusion in AD

4. Sensor Data Fusion Strategies
  • Strategy 1 - Distributed Architecture
  • Strategy 2 - Centralized Architecture
  • Strategy 3 - Hybrid Architecture

5. Sensor Data Fusion Performance Capabilities
  • Role of Sensor Data Fusion by SAE Level of Automation
  • Sensor Data Fusion Market Trends and Analysis - 2019
  • ADAS Sensor Data Fusion Market Penetration
  • Optimum Sensor Data Fusion Strategy Based on Vehicle Segment

6. Industry-wide Best Practices - Sensor Fusion Suppliers Approach
  • Use Case - AEye and Cartica
  • Use Case - Continental LiDAR Camera Fusion
  • Use Case - Valeo and Mobileye

7. Industry-wide Best Practices - Sensor Fusion OEM Approach
  • Tesla Autopilot
  • Audi AI

8. Influence of Sensor Data Fusion on Key Vehicle Technologies
  • Influence of Sensor Data Fusion Strategies on Vehicle Technologies
  • Vehicle E/E Architecture Topologies for Automated Driving

9. E/E Architecture Evolution
  • Challenges with Existing E/E Architecture
  • Evolution of E/E Architecture
  • Vehicle E/E Architecture Strategies

10. Industry-wide Best Practices - E/E Architecture OEM Approach
  • GM Digital Vehicle Platform
  • Volkswagen - The Digital Transformation

11. Industry-wide Best Practices - E/E Architecture Supplier Approach
  • Aptiv - Smart Vehicle Architecture (SVA™)

12. AD Sourcing Software Strategies
  • Key In-vehicle Software Enabling AD
  • ADAS/AD Sourcing Software Business Models
  • AD Software Platforms
  • Value Chain - AD Software and Technology

13. Growth Opportunities and Companies to Action
  • Growth Opportunity - Investments and Partnerships From OEMs/TSPs
  • Strategic Imperatives for Success and Growth

14. Key Conclusions
  • Legal Disclaimer

15. Appendix
  • Abbreviations and Acronyms
  • List of Exhibits

Companies Mentioned (Partial List)

A selection of companies mentioned in this report includes, but is not limited to:

  • AEye
  • Aptiv
  • Audi
  • Cartica
  • GM
  • Mobileye
  • Tesla
  • Valeo
  • Volkswagen