The Future of Intelligent Mobility and its Impact on Transportation

The Future of Intelligent Mobility and its Impact on Transportation

Beyond Automated Driving—Congestion Cut by 25% and Pollution 15% by 2035

RELEASE DATE
26-May-2015
REGION
North America
Research Code: MAF4-01-00-00-00
SKU: AU00009-NA-MR_08563
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Description

While cooperative-driving (V2X-based) is a dependency-based model and automated driving (sensor-based) is a self-sufficient model, these roads converge and complement, thereby leading to intelligent mobility. Moreover, intelligent mobility integrates automated, connected driving with new mobility business models, helping to reduce fatalities, traffic congestion, and per capita carbon footprint. This study outlines the need for intelligent mobility, an ideology that drives people from origin to destination, yet reduces traffic congestion, pollution, and road fatalities by leveraging state-of-the-art automotive technologies. The 3 key regions covered are Europe, North America, and Japan. The study period is 2015 to 2035.

Table of Contents

5 Key Tangible Benefits of Intelligent Mobility

Intelligent Mobility—An Emerging Concept that Revolutionizes Mobility

Intelligent Mobility—A Multi-faceted Sustainable Solution

Impacts of Intelligent Mobility

Key Findings and Future Outlook

Executive Summary—Associated Multimedia

Research Scope

Research Aims and Objectives

Key Questions this Study will Answer

Research Background

Research Methodology

Key Participant Groups Compared in this Study

Detailed Definition of the 3 Key Pillars

Business Case for Intelligent Mobility

Building Blocks of Intelligent Mobility

Intelligent Mobility—Key Stakeholders of Operation

Intelligent Mobility—Key Technology Enablers

Intelligent Mobility—OEMs’ Competencies Compared with Disruptors

Intelligent Mobility Value Stream

Intelligent Mobility—Ecosystem Stakeholder Imperatives

Convergence of Intelligence, Connectivity, and Mobility

Enhanced Mobility—Free-flowing Traffic Every Mile

Enhancing Electric Miles Covered by Vehicle

Enhancing Safety—Achieving the Zero-fatalities Goal

Intelligent Vehicles for Personal and Shared Use

Merging of Personal and Shared Mobility Modes

Application of Smart Navigation in Intelligent Mobility

Applying Gamification to Leverage Normative Driving Behavior

Gamification—Leveraging Crowd Sourced Network

Intelligent Driver Alerts for Intelligent Mobility

Summary of Application and Use Cases of Intelligent Mobility

Interpretation of Intelligent Mobility from 3 Key Regions

Japanese Vision of Intelligent Mobility

European Vision of Intelligent Mobility

North American Vision of Intelligent Mobility

Intelligent Mobility—A Summary of 3 Key Regions

Lack of Collaboration Among Intelligent Mobility Stakeholders

Product Liability and System Reliability

Lack of Clarity in Regulatory Framework and Standardisation

The Need for AI to Address Challenges Ahead of Intelligent Mobility

Summary of Legislative and Industry Challenges

Automated Driving—Application Road Map

Comparative Analysis of Various Automotive Sensors

V2V and V2I: Enablers of Automated Driving

Intelligent Mobility: Deployment Road Map

True Type Learning Algorithm Based Self-learning Car

Functional Road Map Leading to Intelligent Mobility

Summary of Technological Trends

Intelligent Mobility—Summary of OEM Activity

Case Study 1: Audi’s Approach to Intelligent Mobility

Case Study 1: Audi’s Approach to Intelligent Mobility (continued)

Case Study 2: Daimler’s Approach to Intelligent Mobility

Case Study 2: Daimler’s Approach to Intelligent Mobility (continued)

Case Study 3: Ford’s Approach to Intelligent Mobility

Case Study 3: Ford’s Approach to Intelligent Mobility (continued)

Case Study 4: Jaguar Land Rover’s Vision of Self Learning Car

Case Study 4: Jaguar Land Rover’s Vision of Self Learning Car (continued)

Case Study 5: Tesla’s Approach to Intelligent Mobility

Case Study 5: Tesla’s Approach to Intelligent Mobility (continued)

Case Study 6: Toyota’s Approach to Intelligent Mobility

Case Study 6: Toyota’s Approach to Intelligent Mobility (continued)

Case Study 7: Volvo’s Approach to Intelligent Mobility

Case Study 7: Volvo’s Approach to Intelligent Mobility (continued)

Key Conclusions and Future Outlook

The Last Word—3 Big Predictions

Legal Disclaimer

Overview of Cooperative and Autonomous Driving

Learn More—Next Steps

Research Background

Relevant Research

Market Engineering Methodology

Related Research
While cooperative-driving (V2X-based) is a dependency-based model and automated driving (sensor-based) is a self-sufficient model, these roads converge and complement, thereby leading to intelligent mobility. Moreover, intelligent mobility integrates automated, connected driving with new mobility business models, helping to reduce fatalities, traffic congestion, and per capita carbon footprint. This study outlines the need for intelligent mobility, an ideology that drives people from origin to destination, yet reduces traffic congestion, pollution, and road fatalities by leveraging state-of-the-art automotive technologies. The 3 key regions covered are Europe, North America, and Japan. The study period is 2015 to 2035.
More Information
No Index No
Podcast No
Author Kamalesh Mohanarangam
Industries Automotive
WIP Number MAF4-01-00-00-00
Is Prebook No
GPS Codes 9800-A6,9807-A6,9813-A6,9A57-A6,9AF6-A6,9A3B,9B07-C1,9B13-A6