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Overview

In recent years, autonomous driving and also named as robotaxis have become one of the trending topics in the automotive industry. The automotive, transport, and wider mobility market is undergoing a transformational social, technological, and economic shift, fundamentally changing how people and products are moved. Amidst continued population growth, urbanization, and environmental concerns, new forms of mobility are critical to support tomorrow's population hubs and economic activity.

According to the forecast period of 2022 to 2029, the autonomous luxury vehicle market is anticipated to experience significant growth, with a projected rate of 36.16%. The report by Data Bridge Market Research offers comprehensive analysis and insights into the market, highlighting the factors that are expected to have a prominent influence on its growth during this period. 

Tech giants leading software players, and new mobility startups are also on the verge of reaping the rewards of an entirely new future mobility era. Nowadays, the car is turning into a platform to serve various functions. Therefore, autonomous vehicles are becoming much more software-driven products compared to traditional cars. An autonomous car is a vehicle that is capable of sensing its environment and operating without human involvement. Advancements in sensor technology, LiDAR, and 4D radar imaging among others are paving the way for a fully-autonomous vehicle. These technologies are being used to gather specific data in real-time that enables the vehicle to make timely decisions.

 A human passenger is not required to take control of the vehicle at any time, nor is a human passenger required to be present. Traditional manufacturers and suppliers work extremely hard to continuously shorten development cycles and catch up with the inevitable transition into the new software era. However, collaborative agile working models predominantly known from the software industry and more innovative cooperation management approaches pave the way for tackling these challenges and turning them into opportunities.

What is Autonomous Vehicle?

The Digital Transformation of Mobility: Self-Driven Cars

Deep learning is the core aspect of the automation part of autonomous vehicles. AVs can make calculative decisions based on various training models and real-time data acquisition. Recent deep learning and artificial intelligence developments have enabled self-driving cars to respond to high-risk situations and counter obstacle-tracking problems due to weather conditions. An autonomous car or driverless car is a vehicle that uses a combination of sensors, cameras, radar, and artificial intelligence (AI) to travel between destinations without a human operator. Companies developing and/or testing autonomous cars include Audi, BMW, Ford, Google, General Motors, Tesla, Volkswagen, and Volvo.  

World Wide Autonomous Vehicle Market Scenario

The autonomous vehicle is revolutionizing the consumer mobility experience across the globe. With advances in technology, self-driving cars will be safer than human-operated vehicles. In the U.S., 30,000 lives are lost yearly in motor vehicle accidents, often due to fatigue, human error, and drunk driving.

Nowadays, most cars include basic ADAS (advanced driver-assistance systems) features and can function without these behaviors, potentially saving thousands of lives. Most self-driving cars come with an automated emergency braking system that is designed to detect when the car is approaching danger, such as a sudden stop in traffic. Additionally, automated emergency braking systems can be configured to detect and respond to pedestrians, cyclists, or other vehicles on the road. The first self-driving cars were launched from Waymo in 2018 to provide billions of people with safer, cleaner, and more convenient mobility. Waymo's self-driving fleet of 600 cars has driven more autonomous miles than any competitor. In fact, in October 2018 the fleet had driven over 10 million miles on public streets in 25 cities though the focus has been on the streets of Mountain View (CA), Austin (TX), Kirkland (WA), and Phoenix (AZ). In August 2018, the ride-hailing company Lyft announced that its customers have paid for over 5.000 self-driving rides in Las Vegas using its mobile app. The service was launched in Las Vegas in January with 30 BMW cars, but the company had 75 cars in its fleet back then.

Key Strategies Adopted by Automotive Manufacturers

Level of Automation in Self-Driven Vehicle

The Society of Automotive Engineers (SAE) defines 6 levels of driving automation ranging from 0 (fully manual) to 5 (fully autonomous), which the U.S. Department of Transportation has adopted.

Level 0 (No Driving Automation)

Vehicle equipped with no automated features and the driver is in the complete control of the vehicle

Level 1 (Driver Assistance)

Vehicle equipped with one or more primary automated features such as cruise control but requires driver to perform all other tasks

Level 2 (Partial Driving Automation)

Vehicle equipped with two or more primary features such as adaptive cruise control. The vehicle can control both steering and accelerating/decelerating. Here the automation falls short of self-driving because a human sits in the driver's seat and can take control of the car at any time. Tesla Autopilot and Cadillac (General Motors) Super Cruise systems both qualify as Level 2

Level 3 (Conditional Driving Automation)

Vehicle equipped with features that allow the driver to relinquish vehicle's critical safety function depending on traffic & environmental condition. The driver is expected to take over the control of vehicle given the constraints of automated features after a transition period

Level 4 (High Driving Automation)

 Level 4 vehicles can operate in self-driving mode and the key difference between Level 3 and Level 4 automation is that Level 4 vehicles can intervene if things go wrong or there is a system failure.

For instance,

  • NAVYA, a French company, is already building and selling Level 4 shuttles and cabs in the U.S. that run fully on electric power and can reach a top speed of 55 mph
  • In November 2019, Volvo and Baidu announced a strategic partnership to jointly develop Level 4 electric vehicles that will serve the robo taxi market in China

Level 5 (Full Driving Automation)

Fully autonomous vehicle that monitors roadway conditions and performs safety critical tasks throughout the trip with or without driver present.

Source: SEA

The Key Technology of Self-Driving Car or Autonomous Vehicles

Automatic control, architecture, artificial intelligence, computer vision, and many other technologies are integrated into the self-driving car, which is a product of highly developed computer science, pattern recognition and intelligent control technology.

Challenges Faced By Autonomous Cars

The primary hurdle that level 5 autonomous vehicles faces is that the technology isn't advanced enough to create a true level 5 autonomous vehicle. General Motors' Cruise test vehicles and the Nuro cars are just the first steps in the development of level 5 cars. The public's mistrust of driverless vehicles is another obstacle level 5 autonomous vehicles must overcome. The current level 3 cars have been involved in accidents, which raises genuine concerns regarding safety with level 5 cars as they are entirely autonomous. Apart from these, many challenges are still faced in designing fully autonomous system for driverless cars.

Self-driving cars struggle to interpret unusual situations, like a traffic officer waving vehicles through a red light. Simple rule-based programming won't always work because coding for every scenario in advance is impossible. Hence, the idea of a "driverless or autonomous" vehicle on the road has intrigued people from every sphere of living as there are a lot of control-related issues with self-driving cars, and a lot of moving factors that need to be managed and regulated simultaneously while driving.

Top Autonomous Vehicle Ready Countries

The continuing evolution of automotive technology, including driver assistance technologies and automated driving systems, aim to deliver even greater safety benefits. The world has been taken up by autonomous vehicles, and their development is progressing incredibly. While the Netherlands is considered as the emerging leader in this autonomous vehicles readiness index due to its excellent road infrastructure, a highly supportive government, and enthusiastic adoption of electric vehicles, Singapore tripped the United States to rank second largely owing to the amendment to its road traffic act allowing self-driving vehicles to be tested on public roads.

Table 1: Autonomous Vehicle Readiness Index

Country

Technology & Innovation Rank

Infrastructure Rank

Policy & Legislation Rank

Consumer Acceptance

Overall Rank

The Netherlands

4

1

3

2

1

Singapore

8

2

1

1

2

U.S.

1

7

10

4

3

Sweden

2

6

8

6

4

U.K

5

10

4

3

5

Germany

3

12

5

12

6

Canada

6

11

7

7

7

Source: Geospatial Media and Communications

Advantages of autonomous vehicles

Table 2: Autonomous Vehicles Potential Benefits and Costs 

              Benefits

           Costs/Problems

Reduced drivers stress and increased

Productivity

 

Requires additional vehicle equipment, services and fees

Reduces costs for taxis

services and commercial transport drivers

 

Additional crashes caused by system

failures, platooning, higher traffic speeds, additional risk-taking, and increased total vehicle travel

 

Reduces demand for parking at destinations

May require higher roadway design and maintenance standards

Could facilitate car sharing and ridesharing, reducing total vehicle ownership and travel, and associated costs

 

Optimistic predictions of autonomous driving may discourage other transport improvements and management strategies

Source:

Autonomous vehicles can reduce driver's stress and tedium, and increase their productivity, allowing passengers to work while traveling. However, for safety occupants should wear seatbelts, restricting use of in-vehicle beds, and like any confined space, vehicle interiors are likely to become cluttered and dirty. Additionally, autonomous vehicles can provide independent mobility for people who, for any reason cannot or should not drive. This directly benefits those travelers, and by improving their access to education and employment opportunities, can increase their productivity and reduce chauffeuring burdens on their family members and friends.

Challenges Associated With Autonomous Vehicles

Autonomous vehicles require various equipment and services for proper functioning. Since failures could be deadly, autonomous vehicles need robust and redundant components installed and maintained by specialists, increasing maintenance costs. Currently, optional vehicle accessories such as remote starting, active lane assist, and safety cameras, typically cost several thousand dollars, and subscriptions to navigation and security services, such as OnStar and TomTom, cost hundreds of dollars per year. Upgrading to Tesla's Full Self-Drive (FSD) services, which provide limited autonomous operation, cost USD 15,000, and in 2022 owners sued Tesla for false advertising of its availability and benefits. Vehicle owners will probably need to subscribe to frequent software updates and navigation mapping services.

Most autonomous cars use three technologies to navigate, LiDAR (Light Detection and Ranging), cameras, and radar. When driving, radar sensors detect the reflections of radio waves from surrounding objects. So a quick calculation of the time required for reflection of the radio waves allows the self-driving car to measure the proximity of nearby objects. But chances are the radio waves transmitted by two or more vehicles in close proximity will interfere with one another, resulting in false signals. ‍Image classification is done by training the convolutional neural network (CNN) to recognize and classify objects. The problem with CNN is that it's not the best solution for images with multiple objects, as the model is likely not to capture all objects. However global positioning system (GPS) can be used to detect the exact position of other autonomous vehicles, but sometimes they are not able to distinguish between few objects such as walls, building, debris, and trees. A self-driving or autonomous car must be able to distinguish its own signals from the rest, hence it is going to be one of the biggest challenges in the coming years.

The legislation is one of the most essential features of autonomous driving. In many cases, state and federal laws are confused about who would be responsible for accidents brought on by these cars. Determining who is at fault in personal injury claims resulting from routine auto accidents is already difficult enough. As there is no distinct definition of the driver in the case of autonomous vehicles, it is more challenging to pin down who caused the accident and what its effects were. Apart from this, in most self-driving cars, software is the key decision-maker and operator. But, the design may vary depending on the manufacturer.

Although the computer vision autonomous driving model has a real-time object detector, there is a potential that its performance will change depending on the weather, lighting, and location it is in. Autonomous vehicles need a lot of various datasets to prevent any potential accidents caused by the aforementioned variables. Self-driving vehicles can calculate distances, and detect traffic signals, other vehicles, and pedestrians by employing LiDAR sensors and cameras in conjunction with data from three-dimensional (3D) maps and computer vision technology. In order to ensure the safety of the passengers and the vehicle, depth estimation is essential. Although several other tools play key roles, such as LIDAR and camera radar, backing them up with a stereo vision is helpful. However, this makes room for many other issues, such as the camera arrangement, as the distance between lenses and the sensor can be different for each vehicle, making the depth estimation system more challenging.

Regulatory Authorities across the Major Country

Impact of Covid-19 in Autonomous Vehicle Market

The COVID-19 pandemic has brought about an enormous amount of change in daily lives, so the automotive and transportation sectors are keeping an eye on how shifts in consumer behavior may affect the adoption of autonomous vehicle (AV) technologies across all sectors of the economy. The COVID-19 pandemic has influenced the operations of several OEMs, from production to R&D. While there may be a short-term disruption to AV development and roll-outs, this disruption may open up new opportunities for AV technology adoption within consumer segments and speed adoption in various commercial segments as AV technology is seen as a crucial component of responding in times of emergencies. COVID-19 also is reshaping consumer attitudes to public transit in ways that may benefit AV technology in the long-term. While consumer's hesitation towards new car purchases might be driving OEMs to pause AV development, the potential for AV adoption by logistics companies, delivery companies, and the food service industry might provide the OEMs and other AV participants with the market need to propel AV technology to the next level. In a world where staying healthy currently means staying far from our fellow citizens, self-driving long-haul trucks, cross town delivery vehicles, and robotic food delivery seem more appealing than ever. 

As COVID-19 puts the human side of transporting goods into the spotlight, logistic companies need self-driving systems in real-time. While cost savings and non-stop transit of goods are factors, the ability of COVID-19 to pause the shipment of goods has spotlighted the human factor of transporting goods as a weak link in our national supply chain for goods.  In emergencies, the ability to efficiently and reliably transport goods throughout the supply chain is more important than ever before, especially in panic buying and supply constraints. Additionally, the automotive sector's reliance on just-in-time delivery cannot afford supply disruption due to trucking and logistics disruptions. While consumer demand for new and used car buying may have momentarily delayed the adoption of AV systems in the consumer segment, the COVID-19 pandemic has highlighted how important AV is throughout day-to-day commerce and the logistics industry.

Conclusion

Autonomous vehicles (AV) are considered one of the most disruptive technology innovations due to issues of customers' acceptance based on safety, and ethics among others. AVs are changing how the world sees vehicles and human mobility and being a significant technological innovation in the automotive industry. They can bring a variety of benefits, such as an increase in mobility, a reduction in the amount of resources consumed, a lower level of emissions, a decreased need for parking spaces, and an increase in traffic safety. Although the emergence of helpful applications has enabled AV to solve a number of traffic problems, it is agreed that long-term human interaction will be necessary in certain traffic situations, vehicle maintenance, and when the about a self-driving mode cannot be utilized.

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According to the forecast period of 2022 to 2029, the semi-autonomous and autonomous market is anticipated to experience significant growth, with a projected rate of 3.8%. The report by Data Bridge Market Research offers comprehensive analysis and insights into the market, highlighting the factors that are expected to have a prominent influence on its growth during this period.

In the full version of report, Data Bridge will provide market size in terms of Value (USD Million) or customize as per client requirements


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