The Digital Transformation of Mobility: Self-Driven Cars

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. 

• https://www.databridgemarketresearch.com/reports/global-autonomous-luxury-vehicle-market

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?

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

• Baidu (the "Chinese Google") has been developing self-driving cars since 2014, conducting test driving in Beijing since early 2018, and expects to have fully autonomous cars on the streets in 2024
• In February 2023, Renault- Nissan and Mitsubishi entered into a binding framework agreement to collaborate on several new vehicle projects, including new SUVs shared by Renault Group and Nissan, and a new Nissan car model derived from the Renault Triber SUV
• In May 2020, PSA group partnered with Aptiv (NuTonomy) to launch self-driving taxis in Singapore. Under this, Aptive has installed its software, specialized sensors, and computing platforms into Peugeot 3008 SUVs that PSA's innovation teams have customized.
• In October 2018, Toyota partnered with SoftBank to create a range of services using self-driving vehicles, from food and goods delivery to medical check-ups thereby adapting into the era of autonomous driving
• In January 2018, Hyundai Motor Company and Cisco had revealed a new technology roadmap to enable next-generation in-vehicle networking with security. Hyundai and Cisco have created a new platform that provides highly secure access to all the data in the vehicle. The platform utilizes the first generation of 1Gps architectures and Automotive Ethernet with QoS, which allows for maximized bandwidth, more capabilities

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.

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.

• Car Navigation System: In the car navigation system, the vehicle's geographic information system and global positioning system (GPS) are equipped to receive the location information such as longitude and latitude from the satellite. These information, together with the road information generated by location system and digital map database, serve as the source data inputted into the map-matching model, where the intelligent path planning algorithms) are utilized to enable the path planning calculation.
• Location System: The primary goal of the location system is to identify the location of the vehicle, which can be divided into relative, absolute, and hybrid locations. For relative location, the current position of the self-driving car is obtained by adding the moving distance and direction to the prior position. The absolute location method is used to locate the vehicle's position according to the information obtained from positioning system. A common positioning system is the satellite-based system, such as GPS, GLONASS, Galileo, Beidou and so on. However, the satellite signal is prone to interference from the weather conditions and urban environment, such as building and mountain, which will cause error and noise in the location signal, and thus the measured absolute location is not accurate. The hybrid location, which combines the characteristics of the above two locating methods, is the most common method used in obtaining the position of a self-driving car.
• Electronic Map (EM): EM (Electronic Map) is used to store digital map data, which mostly consists of geographic information, traffic data, building data, traffic signs, road facilities, etc. The majority of the EMs utilized in self-driving cars today are human-designed EMs. In the future, it is anticipated that customized EMs for self-driving vehicles will be developed, including ones that automatically recognize road signs and interact among themselves.
• Map Matching: Map matching, the foundation of path planning, determines the location of the car by combining the geographic data from GPS and the map data from EM. The advanced fusing approach is used during the calculation to fuse the longitude, attitude, or other coordinates information into the EM. From a practical perspective, the output of the car location should be precise and timely.

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.

• Road conditions: Road conditions could be highly unpredictable and vary from place to place. In some cases, there are smooth and marked broad highways, and in other cases, road conditions are highly deteriorated. Lanes are not defined; there are potholes, mountainous and tunnel roads where external signals for direction are not very clear and likewise.
• Traffic conditions: Autonomous vehicles must go on public roads and navigate various traffic situations. They would have to share the road with other autonomous vehicles while also navigating many pedestrians. Numerous emotions are present anywhere humans are involved. Highly controlled and self-regulated traffic is possible. However, there are instances where someone may be violating driving laws frequently. An object may turn up in unexpected conditions. In the case of dense traffic, even the movement of few cms per minute does matter. One can't wait endlessly for traffic to clear and have some precondition to start moving automatically.
• Accident Liability: The most important aspect of autonomous cars is accidents liability. People often think about who is liable for accidents caused by a self-driving car? In the case of autonomous cars, the software will be the main component that will drive the car and will make all the important decisions. While the initial designs have a person physically placed behind the steering wheel, newer designs showcased by Google, do not have a dashboard and a steering wheel. Additionally, due to the nature of autonomous cars, the occupants will mostly be in a relaxed state and may not be paying close attention to the traffic conditions. In many situations where the driver's attention is needed, by the time they need to act, it may be too late to avert the situation and may occur accident.
• Radar Interference: Lasers and radar are used by autonomous vehicles to navigate. While the sensors are installed on the vehicle's body, the lasers are mounted on the roof. Radar operates on the basis of detecting radio wave reflections from surrounding objects. When driving, a car continuously emits radio frequency waves that are reflected upon nearby vehicles and other objects within range. The reflection's time is measured to determine the distance between the car and the object. When on the road, a car will continuously emit radio frequency waves, which get reflected from the surrounding cars and other objects near the road. Hence the question arises when this technology is used for hundreds of vehicles on the road, will a car be able to distinguish between its own (reflected) signal and the signal (reflected or transmitted) from another vehicle? Even if multiple radio frequencies are available for radar, this frequency range is unlikely to be insufficient for all the vehicles manufactured.

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

Source: Geospatial Media and Communications

Advantages of autonomous vehicles

Table 2: Autonomous Vehicles Potential Benefits and Costs 

Source:

• Reduced Driver Stress, Improved Productivity, and Mobility

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

• Ownership and Operating Costs

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.

• Dealing with Potential Radar Interference

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.

• Navigating through the Legal Complications

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.

• Weather & Road Conditions

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

• Canada:  The federal government has focused on ensuring consistency across all jurisdictions while provincial, territorial and municipal governments are taking steps to promote AV development. In Ontario, companies must obtain consent to Ontario's AV Pilot Project regulations to put an AV (SAE level 4-5) on the road.
• China: On March 9, 2020, the Ministry of Industry and Information Technology in China released the recommended draft of national standards for automobile driving automation classification on the official website.
• Germany: In November 2020 Federal Minister of Germany presented a draft bill for the law on autonomous driving which will create a legal framework for Level 4 and Level 5 vehicles.
• Hungary: Hungary's Minister of Technology and Innovation introduced the Artificial Intelligence Strategy of Hungary in September 2020 for the next 10 years. The strategy envisions the creation of a supportive development environment for research and infrastructure required for autonomous driving technologies.
• United States: The most recent action by the Department of Transportation in the United States is the release of the Automated Vehicles Comprehensive Plan. In part, the plan looks back over what the Department has done in relation to AVs during Elaine Chao's time as the Secretary of Transportation. However, it also lays out several steps the Department plans to take going forward. The plan provides three broad principles for the Department going forward in regard to autonomous vehicles: protect users and communities, promote efficient markets, and facilitate coordinated efforts. Additionally, the Department of Transportation and the National Highway Traffic Safety Administration (NHTSA) in the United States issued an advanced notice of proposed rulemaking in November requesting comments on a new generation of safety standards for autonomous vehicles. According to the NHTSA, "The framework would objectively define, assess, and manage the safety of ADS performance while ensuring the needed flexibility to enable further innovation."
• South Korea: South Korea's Autonomous Vehicle Act became effective on May 1, 2020. The Act provides the necessary support and infrastructure for introducing, spreading and safely operating AVs. The Act also regulates the necessary requirements in relation to AVs.

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.

• https://www.databridgemarketresearch.com/reports/global-semi-autonomous-and-autonomous-vehicle-market

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