Autonomous Vehicles - Quick Guide

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Introduction to Autonomous Vehicles

Autonomous Vehicles are modern vehicles or cars that can drive with minimal or no human intervention. Such vehicles are also termed as self-driving vehicles, or self-driving cars, or robocars.

For self-driving, autonomous vehicles use modern tools and technologies like smart sensors, cameras, artificial intelligence, machine learning, etc. These components and technologies allow the vehicles to see, feel, and understand their surroundings and perform important functions like driving, braking, steering, etc. autonomously.

The primary objective of developing autonomous vehicles is to make driving more comfortable and safer.

In this introductory chapter on autonomous vehicles, we will learn the fundamentals of autonomous vehicles and their importance and impact in transportation industry.

What is an Autonomous Vehicle?

An autonomous vehicle is a car or any other vehicle that can drive on its own, without any human driver's intervention. These vehicles use sensors, cameras, and AI technologies to interpret their surroundings.

Autonomous vehicles are capable in performing all driving functions, from steering to speed control and braking, autonomously according to the traffic conditions on roads. An autonomous vehicle is basically a combination of a traditional car and smart technologies.

These vehicles make the driving comfortable and safer, and reduce the road accidents significantly.

Architecture of Autonomous Vehicle

The architectural block diagram of an autonomous vehicle is shown in the following figure −

In this block diagram, we can see that an autonomous vehicle consists of several components that work together and allow the vehicle to understand its surroundings, make decisions, and control the vehicle navigation. Each component of this typical autonomous vehicle is explained here −

Sensors

Sensors are nothing but components that capture data about vehicles surrounding environment. They detect and measure position and size of nearby objects, road conditions, traffic density, etc. Sensors act as the eyes and ears of the autonomous vehicle and allow the vehicle to see and feel its environment.

The most commonly used sensors in autonomous vehicles include cameras, LiDAR, RADAR, ultrasonic sensors, GPS, etc. The sensed data is then sent to the perception unit for interpretation.

Perception Unit

This component of autonomous vehicles is responsible for processing and analyzing the sensors data. It basically interprets the surrounding environment of the vehicle and allows the vehicle to detect obstacles, recognize road signs, identify road conditions, understand lane markings, etc.

Therefore, the perception unit creates an exact view of the surrounding that helps the vehicle to make driving plans and take decisions.

Planning and Decision Module

This module uses information provided by the perception unit to make path plans and decisions. It performs functions like path planning by finding the best route, avoiding obstacles, decision-making for self and safe driving, etc.

Planning and decision module also uses external data from maps, GPS, user, etc. to make right decisions about steering, acceleration, braking, etc.

Motion and Vehicle Control Unit

This component converts the instructions received from the planning and decision module into physical actions to control the vehicles operation. It works by sending commands to actuators to perform functions like steering, acceleration, braking, etc.

Actuators

Actuators are the components that control the physical actions of the vehicle like acceleration, turning the wheels, stopping the car, etc. These actions are performed as per the commands received from the motion and vehicle control unit.

Supervision System

This component is responsible for monitoring or supervising the entire vehicle system. It ensures that all other components of the autonomous vehicle are functioning as desired.

It performs functions like system diagnostics, failure or malfunctioning prevention, continuous monitoring, etc.

How Do Autonomous Vehicles Work?

Autonomous vehicles use advanced technologies like smart sensors, artificial intelligence, etc. to operate and navigate, without any human intervention. These advanced technologies are developed using huge amount of data from GPS, maps, image processing, etc.

Technologies like machine learning and neural networks are used to determine the patterns in this data that allow autonomous vehicles to understand their surroundings.

Autonomous vehicles collect information using sensors like LiDAR, RADAR, cameras, GPS, etc. to recognize the elements and objects present in their surroundings like other vehicles, road signs, obstacles, traffic lights, road lanes, etc.

Using this data, vehicles create a map of their surroundings and plan the paths by identifying and selecting safest and fastest route to the destination. Some more advanced autonomous vehicles use a technology named, geofencing, to create virtual boundaries with GPS and to ensure that the vehicle will stay within the safe driving area.

Features of Autonomous Vehicles

The following are some key features of autonomous vehicles available in the market −

• Autonomous vehicles provide hands-free steering that can center the car without drivers intervention.
• Autonomous vehicles have adaptive cruise control (ACC) that can automatically maintain a safe distance between vehicle and the vehicle in front of it.
• Many autonomous vehicles support geofencing that keep the vehicle within a safe lane area.
• Autonomous vehicles also have a self-parking feature that can automatically steer, accelerate, and guide the vehicle using sensors to operate it in a parking space.
• Autonomous vehicles have a lane-change assistant that monitors the surrounding traffic of the vehicle and helps in a safe lane change.
• Autonomous vehicles also have an automatic emergency braking (AEB) system to detect imminent collisions and stop the vehicle to prevent accidents.

Conclusion

In this chapter, we explained in detail the basic meaning, structure, and the working of a typical autonomous vehicle. Lets move to the next chapter and discuss the different types of autonomous vehicles.

Types of Autonomous Vehicles

Autonomous vehicles are classified into several types. The basis of this classification is nothing but the level of autonomy, i.e., how much a vehicle can drive autonomously without need for human control.

In the field of autonomous vehicles, a widely recognized system of classification for autonomous vehicles is used, which is defined by the Society of Automotive Engineers (SAE).

According to SAE, autonomous vehicles are broadly classified into six distinct classes or levels, and they are −

• Level 1 Vehicles
• Level 2 Vehicles
• Level 3 Vehicles
• Level 4 Vehicles
• Level 5 Vehicles

This classification of autonomous vehicles provides a clear difference between development and adoption of self-driving technologies in autonomous vehicles. Lets discuss each class of autonomous vehicles in more detail.

Level 0 Vehicles

Vehicles that do not have any automation capabilities in driving and need a human driver for controlling them are categorized as level 0 vehicles. Thus, level zero is a No Automation level.

However, these vehicles support some basic features such as,

• Blind spot warning
• Forward collision warning, etc.

Level 1 Vehicles

Level 1 vehicles provide some basic automation functions to provide driver assistance. Although, a human driver is needed for performing most driving functions and vehicle control. In level 1 vehicles, one automation function can take place at a time.

The automation features that level 1 vehicles support include −

• Adaptive cruise control (ACC) to control the speed of the vehicle, but the steering must be managed by the human driver.
• Lane keeping assist (LKA) to steer the vehicle to maintain the lane, but the speed must be control by the human driver.

Level 2 Vehicles

Level 2 is known as partial automation level in vehicles. Level 2 vehicles are capable in managing both speed and steering at the same time depending on the driving conditions. Although, a human driver still remains engaged in the driving activities for safe driving.

The key features included in the level 2 vehicles are −

• Tesla autopilot
• GM super cruise
• Audi traffic jam assist, etc.

Thus, the level 2 vehicles can handle some basic driving tasks on highways, but the human driver must continuously monitor the driving functions and take actions immediately.

Level 3 Vehicles

Level 3 autonomy is also known as conditional automation in vehicles. The level 3 vehicles can perform most driving functions in a specific driving environment. But a human driver's intervention is required if the vehicle request it.

Therefore, in level 3 vehicles, the human driver can temporarily disengage from the driving activities, but must be ready to resume it when requested.

The following key functions the level 3 vehicles can perform −

• Navigation on highways or in traffic jams
• Steering control
• Braking and acceleration, etc.

Level 4 Vehicles

Level 4 is termed as high automation level in vehicles. Level 4 vehicles are capable in self-driving in a specific geofencing area or condition, without any human driver. However, if the vehicle goes outside the specified condition or area, the human driver must be available to take the control, otherwise the vehicle will stop safely.

The key features of level 4 vehicles are listed here −

• If the vehicle is driving within the specified condition or geofencing area, then no driver attention is needed.
• Vehicle can operate without human intervention and stops if goes outside the operational area.

Level 5 Vehicles

Level 5 vehicles support full automation and thus they can operate autonomously in any driving environment. These vehicles do not need a human driver under any condition. Therefore, components like steering wheel, brake pedals, etc. are not necessary in these vehicles because they do not require human intervention at any time.

Level 5 vehicles are currently under development and are not available commercially.

Important Points about Autonomous Vehicles

Some of the important characteristics of autonomous vehicles are highlighted below −

• Human Intervention − When we go from level 0 to level 5, need for human intervention goes down, and the level 5 vehicles do not require any kind of human intervention.
• Driving Environment − Level 0 to level 2 vehicles need a human driver to monitor the vehicle operation in all driving environments, while level 3 to level 5 vehicles can make self-driving decisions depending on the driving environment.
• Sensor-Based Technology − The increased level of autonomy in vehicles needs advanced sensors to understand the surrounding environment.
• Smart Infrastructure − Higher level of autonomy requires smart infrastructure like smart roads, vehicle-to-vehicle and vehicle-to-everything communication, etc.

Conclusion

The classification of autonomous vehicles is done on the basis of level of autonomy defined by SAE. In conclusion, according to SAE, there are six types of autonomous vehicles, from level 0 to level 5. The level 0 are no autonomous vehicles, while level 5 are fully autonomous vehicles. Read the next chapter to learn why we need Autonomous Vehicles.

Why Do We Need Autonomous Vehicles?

Autonomous vehicles are playing a vital role in the transportation industry, road safety, and traffic control. In this technology age, the need for autonomous vehicles (driver-less cars) is being increasing because of the various factors including social, environmental, economic, etc. The primary aim of developing and deploying autonomous vehicles in transportation industries is to overcome inefficiencies, safety issues, accessibility related problems, etc.

In this chapter, we will learn about various factors, making the autonomous vehicle technology an essential component of the modern mobility and transportation.

Why Autonomous Vehicles are Needed?

In this section, we have highlighted some of the major that encourage the development and deployment of autonomous vehicles in mobility −

Safety Enhancement

Safety Enhancement is one of the most significant need that drives the development of autonomous vehicles. It is well known that human errors while driving are the main causes of road accidents. The key reasons behind these human errors include poor judgement, distractions, intoxication, and fatigue. Autonomous vehicles use advanced technologies like AI and tools like smart sensors and cameras to monitor the surrounding environment and detect the obstacles faster than human drivers. Hence, autonomous vehicles significantly reduce the human driving errors and hence the road accidents.

Also, autonomous vehicles are equipped with a technology called ACAS (Advanced Collision Avoidance System) that makes the combined use of smart sensors like LiDAR, RADAR, cameras, etc. to detect possible collisions in advance and avoid them by controlling the speed and applying brakes autonomously. This system is meant for maintaining a safe distance between the vehicle and the vehicle in front of it.

These two practices significantly reduce both frequency and severity of road accidents and enhance the road safety.

Increased Mobility and Accessibility

Autonomous vehicles make the driving available for physically challenged and aged people who cannot drive otherwise. Therefore, mobility and accessibility are another major needs for development of autonomous vehicles.

Persons who cannot drive due to age or any physical disability can take the benefit from autonomous vehicles. Autonomous vehicles increase their mobility and independence in terms of transportation by providing door-to-door transportation facility without need for any assistance. Therefore, autonomous vehicles allow these people to involve in social activities and access services like healthcare, shopping, etc.

In rural and non-developed areas, where public transportation facilities are not available, autonomous vehicles can become a reliable and affordable option. In these areas, shared autonomous vehicles like robotaxi, autonomous cabs or buses can be used to provide transportation services either regular or on-demand basis. This facility can address the barriers in access to education, jobs, healthcare, etc. in areas where transportation is not readily available.

Traffic Congestion Reduction

In modern days, one of the biggest issues in transportation is traffic congestion, which results in significant wastage of time, fuel, and productivity. Autonomous vehicles are equipped with smart sensors that allow them to communicate with other vehicles and with the traffic control infrastructure to optimize the traffic flow on roads. This makes the commutes faster and reduces the problem of traffic jams.

Autonomous vehicles are also designed in a way that they strictly follow the traffic rules and avoid inefficient operations like unnecessary lane change, unsafe overtakes, over speeding, etc. This further helps reducing the traffic congestion.

Therefore, autonomous vehicles are also developed and deployed into the transportation infrastructure to reduce the traffic congestion.

Environmental Sustainability

Traditional and driver-operated vehicles produce emissions and pollutants, and they also have limited energy efficiency due to manual operation. This is another reason that arises needs for autonomous vehicles.

Autonomous vehicles give higher energy efficiency and reduces the carbon footprint of transportation. This is possible because autonomous vehicles are capable in maintaining optimal speeds, reducing idle time, and providing smooth acceleration. All these activities cause higher energy efficiency as compared to a human operated vehicle.

Some autonomous vehicles are battery-operated electric vehicles and hence they provide additional environmental benefits as compared to engine operated vehicles.

Autonomous vehicles further reduce the emissions caused due to traffic congestion by reducing stop-and-go traffic conditions, which is a major cause of pollution in urban areas.

Economic Benefits

Autonomous vehicles help in significant cost savings for consumers. As these vehicles optimize the fuel consumption, they reduce the fuel cost for vehicle owners. Additionally, autonomous vehicles improve the road safety and make the accidents less frequent and severe. Hence, they also reduce various costs like insurance premiums, repair costs, healthcare costs, etc.

In freight and logistics industries, autonomous vehicles can operate 24×7 without any break unlike human drivers need. This reduces the transportation time and increases the delivery speeds. Thus, it helps building a cost-effective and high-speed supply chain.

Real-Time Traffic Management

Autonomous vehicles are capable in collecting and processing large amount of traffic data in real-time. Hence, they can help managing the traffic by controlling the traffic signals dynamically. Autonomous vehicles also identify optimized routes to the destination, and prevent the traffic related bottlenecks.

Predictive Maintenance

Autonomous vehicles also reduce downtimes due to breakdowns through predictive maintenance. Autonomous vehicles use advanced diagnostics systems and smart sensors for continuous monitoring of engine health, tyre pressure, and other important components. If there are any potential issues in the vehicle that may cause any serious problems, the diagnostics system sends warnings to the vehicle operator in advance.

Conclusion

To conclude, autonomous vehicles help reduce potential issues in transportation industry and improve safety and mobility. These vehicles also reduce the fuel consumption and impacts on the environment. However, a complete deployment of autonomous vehicles in the transportation sector is subjected to several technical, regulatory, ethical, and reliability concerns.

Read the next chapter to get a good understanding of the advantages of using autonomous vehicles.

Advantages of Autonomous Vehicles

Read this chapter to learn the key advantages of using autonomous vehicles. The most significant advantage of autonomous vehicles is to improve the driving experience and road safety. Let discuss some more advantages in detail.

Advantages of Autonomous Vehicles

The key advantages or benefits of using autonomous vehicles (self-driving vehicles) are explained below −

Improved Road Safety

The primary reason behind most road accidents is the human errors in driving. These errors are mainly caused because they require 360° continuous monitoring of the vehicle's surrounding, which is a tedious task.

Autonomous vehicles are designed by using smart sensors and cameras to perform this task continuously without being tired. These sensors and cameras can accurately detect the obstacles, other vehicles, and road conditions consistently, which is nearly impossible for human drivers.

Hence, autonomous vehicles reduce significant burden on drivers for consistent monitoring of the surrounding environment. Also, the autonomous vehicles are designed to follow traffic rules to avoid potential traffic risks.

All these practices help reducing road accidents, damages, and injuries. Therefore, autonomous vehicles enhance the road safety.

Increased Reliability in Transportation

Autonomous vehicles can operate with minimal or no human instructions and interventions. Hence, these vehicles provide a reliable mode of transportation for those people who cannot drive due to any cause like aging, physical disability, etc. By proving a reliable mobility mean, autonomous vehicles make disable people to access essential services, such as social activities, education, healthcare, etc.

Better Traffic Management on Roads

Autonomous vehicle works on vehicle-to-everything technology to communicate with other vehicles as well as the road infrastructure. Hence, these vehicles can plan trips with optimized routes. They can also regulate their speeds to avoid issues that cause traffic congestion on roads and highways.

Autonomous vehicles are capable in performing braking and acceleration smoothly according to the real-time road conditions. This consistent driving pattern helps managing the traffic to reduce congestion on the roads. Additionally, it enhances the fuel efficiency of the vehicle.

Reduced Carbon Emissions and Pollution

Autonomous vehicles are designed to give higher fuel efficiency that reduces the carbon emissions and minimize the environment pollution. These vehicles become more eco-friendly, when they are equipped with electric powertrains.

Lower Operational and Maintenance Costs

Because autonomous vehicles perform smooth acceleration and braking, this results in reduced wear and tear on the moving components of the vehicle. Therefore, autonomous vehicles require less maintenance and hence the lower maintenance cost. It also extends the lifespan of the vehicle.

Additionally, autonomous vehicles can plan optimized routes for the trips, which increases the fuel efficiency and thus reduces the operational cost involved.

Increased Productivity

The fully autonomous vehicles free up the time from driving. Thus, in these vehicles, passengers can focus on other tasks like reading, relaxation, office work, etc. This increases the person's productivity and hence the quality of life.

Reduced Traffic Violations and Associated Costs

Autonomous vehicles are designed to strictly follow the traffic rules like no red light jumping, no over speeding, etc. By avoiding all these traffic rule violations, autonomous vehicles reduce the fines, accidents, injuries, etc. This lowers the cost involved in these undesirable activities.

Conclusion

The benefits listed above collectively make the autonomous vehicles a transformative technology in the field of mobility and transportation. In conclusion, autonomous vehicles can help us improve road safety, efficiency, and environmental conditions.

Possible Impacts of Autonomous Vehicles

With the increased development and deployment of autonomous vehicles, car manufacturers and the entire automobile industry are facing various challenges and opportunities that are reshaping the traditional automotive landscape.

This chapter is meant for highlighting all the possible challenges and impacts of autonomous vehicles on the auto industry and car manufacturers.

Impacts of Autonomous Vehicles on Car Manufacturers

With advancement in autonomous vehicle technology, the following impacts and challenges are affecting the car manufacturers and the entire auto industry −

Changing Business Models and Revenue Streams

Traditionally, car manufacturers were focused on designing, manufacturing, and selling cars. The selling of cars direct to consumers is one of the primary sources of revenue for all car manufacturers. But with the autonomous vehicle technology, new business models have been created like shared robo-taxi, ride-hailing, subscription-based car services, etc. All these business models are creating recurring revenue models by using autonomous vehicles.

Overall, autonomous vehicles are implementing mobility-as-a-service (MaaS). MaaS is also encouraging partnerships between car manufacturers and technology companies or transport service providers.

New Competitive Landscape

The development of autonomous vehicles is attracting technology companies and startups to automotive industry. Tech companies like Google, Apple, Tesla, and many more are investing heavily in autonomous vehicle technology for creating a new competitive landscape for traditional car manufacturers. Because of this, the traditional car manufacturers are facing a more competitive environment and trying to bring safe and reliable autonomous vehicles to the market.

Vehicle Design and Architecture Modifications

In autonomous vehicles, the needs for human intervention are minimized due to self-driving. Therefore, autonomous vehicle demands a transformed interior design to provide more comfort and features for productivity or entertainment.

Also, the autonomous vehicles are equipped with various advanced sensors and AI technologies, thus car manufacturers have to make necessary modifications in the vehicle design and architecture to optimize the placement of these sensors. This helps ensuring a reliable and safe operation.

Big Data Management

Autonomous vehicles use sensors and AI technologies and hence they generate a huge amount of data. This data is very crucial for enhancing self-driving algorithms, improving road safety, and optimizing the traffic flow on roads.

Regulatory bodies and infrastructure companies can use this big data to understand the driving patterns, traffic flows, and can innovate the ways to improve the road infrastructure.

Impact on Automotive Supply Chain and Manufacturing

Autonomous vehicles also have impacts on the supply chain. Car manufacturers need to collaborate with new suppliers for components used in autonomous vehicles like sensors, AI processors, cameras, etc.

Also, autonomous vehicle technology requires a manufacturing process different from the traditional one. In AV manufacturing, improved quality control and testing procedures are very important for ensuring desired reliability and safety of autonomous vehicles.

Regulatory and Safety Challenges

Autonomous vehicles also present complex regulatory and liability challenges. Therefore, governments and policymakers must work together to create standards and a legal framework to ensure the safe deployment of autonomous vehicles into public roads.

Automakers also need to work with policymakers to establish regulatory standards to govern safe AV operation, liability, safety testing, data privacy, and security while facilitating innovation.

Conclusion

In this chapter, we explained some of the key impacts of autonomous vehicles on car manufacturers and the automakers. The advancement in autonomous vehicle technology is reshaping the automotive industry by changing the business models, vehicle design, and partnerships for the car manufacturers. Lets move to the next chapter to learn about Levels of Autonomy in Driving.

Levels of Autonomy in Driving

Autonomous vehicle technology is not a futuristic dream; instead it has become a rapidly evolving reality. Autonomous or self-driving technology is revolutionizing and transforming the transportation industry. It is increasing the driving comfort and road safety and changing our way of interacting with vehicles.

Self-driving technology has different levels of autonomy. Based on the autonomy levels, vehicles can be classified into six different classes, ranging from fully manual to completely autonomous. This classification of levels of autonomy in driving is done by the Society of Automotive Engineers (SAE).

In this chapter, we will explain each level of autonomy in driving and will understand its technology, capabilities, and current status of use. This understanding of levels of autonomy is important for gaining insights into the future of autonomous vehicles.

What is Autonomy Level in Driving?

The extent to which a vehicle can perform driving functions autonomously, without any human intervention, is referred to as autonomy level in driving. Autonomy level basically describes the involvement of a human driver to control the vehicle operations.

Autonomy level can vary from zero automation to full automation, depending on the vehicle's control capabilities by the human driver. This concept is primarily used to clarify the capabilities and limitations of a vehicle with respect to self-driving technology.

Levels of Autonomy in Driving

The Society of Automotive Engineers (SAE) has defined six levels of autonomy in driving, depending on their features, capabilities, and limitations. These six levels of driving autonomy are explained here in detail −

Level 0 No Driving Automation

The SAE level 0 is termed as No Driving Automation. At level 0 autonomy, the vehicles are fully manual and need a human driver to perform all driving tasks and control the vehicle.

However, these vehicles are equipped with some basic safety and warning features like emergency braking and lane change warning. These features just assist the human driver, but do not provide any driving automation in the vehicle. Most cars and other vehicles, we see on the roads today are level 0 autonomous vehicles and fully controlled by the human drivers.

Level 1 Driver Assistance

The SAE level 1 is termed as Driver Assistance. At level 1 autonomy, the vehicles are capable in providing driver assistance with either steering or acceleration but not both at the same time. Hence, in these vehicles, the driver requires to have a constant oversight on the vehicles operation.

The most commonly used technologies in level 1 vehicles include adaptive cruise control (ACC) and lane-keeping assistance. ACC is responsible for maintaining a constant speed and safe distance between the vehicle and the vehicle in front of it. Hence, cars with features like cruise control, park assistant, etc. fall in this category.

Level 2 Partial Driving Automation

The SAE level 2 is referred to as Partial Driving Automation. The level 2 vehicles are capable in providing assistance with both steering and acceleration simultaneous under a specific driving environment. However, it is important that the human driver must continuously monitor the surrounding and be ready to take over when needed.

The level 2 vehicles use Advanced Driver Assistance Systems (ADAS) to perform autonomous steering, speed control, and breaking functions. Some common examples of currently available level 2 autonomous driving systems include Tesla's Autopilot, GMs Super Cruise, etc.

Level 3 Conditional Driving Automation

The SAE level 3 is referred to as Conditional Driving Automation. The level 3 vehicles are capable in detecting environment conditions dynamically and make decisions based on that. For example, these vehicles can autonomously accelerate past a slow-moving vehicle. However, a human driver is still required for continuous oversight. The driver must be ready to take control if the autonomous system is unable to perform the function.

Vehicles of this category are equipped with advanced sensors and AI (artificial intelligence) technologies. Using these technologies, the level 3 vehicles can perform traffic navigation and speed adjustment, but under the supervision of a human driver. Audi's A8L with Traffic Jam Pilot is a common example of level 3 autonomous vehicle.

Level 4 High Driving Automation

The SAE level 4 is termed as High Driving Automation. At level 4, the vehicles are capable in performing full driving automation within a specified geofencing area. Also, these vehicles do not need a human driver under normal conditions and the specified area.

In level 4 vehicles, the most widely used technologies include HD mapping, robust artificial intelligence technologies, and redundant systems. All these technologies together allow this category vehicles to operate safely without any human intervention. Although, the level 4 vehicles are still unable to operate in unrestricted environments autonomously.

Examples of level 4 autonomous vehicles include Waymo's driverless taxi and Navya's autonomous shuttle.

Level 5 Full Driving Automation

The SAE level 5 is the Full Driving Automation. At level 5, the vehicles are capable in performing all driving tasks under all conditions without any human intervention. Hence, level 5 vehicles are referred to as completely autonomous vehicles. These vehicles do not have human driver controls like steering wheel, brake pedals, etc.

In these vehicles, advanced artificial intelligence technologies and smart sensor suite are used to provide accurate and reliable monitoring of the surrounding environment. These vehicles can understand the road conditions, traffic flow, road signs and signals autonomously. Therefore, level 5 vehicles can be termed as completely driverless cars or self-driving cars.

Summary of Driving Autonomy Levels

Conclusion

In conclusion, the SAE has defined total six driving autonomy levels. Each level has its own features and capabilities. Today, most vehicles that we see on the roads fall under the level 2 category of driving autonomy, as they provide only assistance to human driver rather than full driving automation.

Level 3 vehicles are close to production and commercialization but pending for regulatory approval in many countries. Level 4 and Level 5 vehicles are under the development and testing stage. However, with the technological advancement, full autonomy is expected in the near future. Lets move to the next chapter and learn about Technologies Inside Autonomous Vehicles.

Autonomous Vehicles - Technology

Autonomous vehicles or self-driving cars use a wide range of advanced technologies to perform driving tasks without any human intervention or assistance. Thus, these vehicles are completely dependent on various technologies that work together to perform control, monitoring, and decision-making functions on the roads.

In this chapter, we will discuss about each technology used in autonomous or self-driving vehicles.

Technology inside Autonomous Vehicles

The following are some key technologies behind the autonomous working of a self-driving car −

Advanced Sensors

Sensors act as the eyes and ears of the autonomous vehicles. In autonomous vehicles, a variety of advanced sensors are used to collect data from the surrounding environment. These sensors help the vehicle to understand its surrounding elements like other vehicles, obstacles, road signs, pedestrians, etc.

The following are some primarily used types of sensors in an autonomous vehicle −

LiDAR

LiDAR stands for Light Detection and Ranging. LiDAR is a laser-based sensor technology that used in autonomous vehicles to create a 3D map of the surrounding environment. This sensor allows the vehicles to identify nearby objects and measure their distance from the vehicle.

LiDAR is known for its high accuracy and depth perception and it is very helpful in detecting objects at night or in bad weather conditions. However, it is a very expensive sensor technology and does not work properly in heavy rain or snow fall conditions.

RADAR

RADAR stands for Radio Detection and Ranging. It is also a sensor technology that makes use of radio waves and is used in autonomous vehicles to detect the position and speed of nearby objects like other vehicles on the road.

The most significant benefit of the RADAR technology is that it can work well in most weather conditions and is capable in measuring speed of moving vehicles in the surrounding environment of the autonomous vehicle.

However, due to limited resolution, RADAR may not be able to detect small objects. It is also less accurate in recognizing specific shapes of objects.

Cameras

Cameras are used in autonomous vehicles to allow them to see the surrounding environment. They are responsible for detecting road signs, traffic lights, lane markings, and other road infrastructure components.

Cameras are capable in providing detailed images of the surrounding and are also less expensive than LiDAR and RADAR. However, they do not work properly in low light, rain, fog, and other poor weather conditions. Additionally, complex algorithms are required for accurate interpretation of visual data captured by cameras.

Ultrasonic Sensors

Ultrasonic sensors work by emitting sound waves and used to detect objects in a short and closed range like in parking space. These sensors are used in autonomous vehicle to perform functions like auto-parking. These sensors are less expensive but they work withing a limited range.

Artificial Intelligence (AI) and Machine Learning

Artificial intelligence and machine learning act as the brain of the autonomous vehicle and perform functions like decision making, self-learning from actions, understanding surroundings, etc.

Autonomous vehicles use AI-based neural network that process and interpret data captured by sensors. AI allows autonomous vehicles to identify objects, make predictions and decisions, plan and control the movements of vehicle, etc. Overall, AI and machine learning help the autonomous vehicles to make real-time decisions accurately.

However, AI and machine learning are data-based technologies and require a large amount of data to make decisions and release commands.

Computer Vision

Computer vision is also a part of artificial intelligence which involves tasks like image and video processing. It is used in autonomous vehicles to process camera's visual data to understand the surrounding environment.

Computer vision allows autonomous vehicles to identify obstacles, lane markings, and traffic signs and signals. It is responsible for ensuring accurate movements of the vehicle and provide safe autonomous driving even in a complex environment.

However, this technology requires advanced hardware and complex programs to handle unexpected events and poor visibility conditions.

GPS and Maps

Autonomous vehicles need precise data about locations and routes to navigate accurately. This data is provided by GPS (Global Positioning System) and high-definition maps.

GPS is a satellite-based system that integrated into autonomous vehicles to determine their exact locations. It is also integrated with high-definition maps to help the vehicle understand where it is and identify optimized routes. Although, the GPS system becomes less accurate in congested urban areas or in tunnels due to signal blockage.

The high-definition maps provide a detailed layout of roads and the infrastructure along with information about lane markings, traffic signals and signs, etc. Maps are essential for planning optimized routes and safely navigate the vehicle. However, the rapid infrastructure changes require updating the maps which is a challenging and costly task.

Vehicle-to-Everything (V2X) Communication

Communication or connectivity is one of the most crucial technologies in autonomous vehicles. It allows vehicles to communicate and exchange data and information with other vehicles, road infrastructure and servers.

In autonomous vehicles, there are several different types of communications used, such as vehicle-to-vehicle communication, vehicle-to-infrastructure communication, communication with cloud servers.

The vehicle-to-vehicle (V2V) communication allows autonomous vehicles to share information like speed, direction, etc. with other vehicles. It is responsible for providing a safe navigation on roads.

The vehicle-to-infrastructure (V2I) communication allows vehicles to communicate with infrastructure components like road signs, traffic signals, etc. This communication ensures better traffic management and reduces congestion on roads. However, it is very challenging and expensive to implement because it requires major changes in the existing infrastructure.

Cloud connectivity or communication with cloud servers enable autonomous vehicles to access maps, location details, and traffic updates. This communication ensures that the vehicles are up-to-date with the latest information. Although, this communication is subjected to network failures and connectivity issues that could disturb the vehicle operations and impact the road safety.

Path Planning System

It is a software component of the autonomous vehicle that helps planning the optimized routes and provides directions to navigate safely. This system is crucial for smooth and safe driving of the vehicle autonomously.

Motion Control System

In autonomous vehicles, the motion control system receives input from AI system and performs actions like steering, acceleration, braking, etc. accordingly. This system is responsible for providing precise control for safe and smooth operation of the vehicle.

Edge Computing

Autonomous vehicles require real-time data processing to perform actions quickly. Edge computing enables them to do so. This technology implements capabilities in the autonomous vehicle to process data locally without being relying on the cloud server. Edge computing plays an important in reducing delay and makes the autonomous vehicles faster in decision-making.

Cybersecurity Systems

Autonomous vehicles use advanced connectivity, software, and data, which are vulnerable to cyberthreats. Hence, cybersecurity systems like encryption, firewalls, intrusion detection systems, etc. are used in these vehicles to protect them from all kinds of cyber threats. These systems are essential for protecting the vehicles sensitive data and hacking.

Conclusion

Autonomous vehicles are an interesting blend of various advanced technologies that enable these vehicles to see, think, and take actions, without need for any human driver. Each of these technologies play an important role in self-driving of these vehicles safely.

Risks in Autonomous Vehicle Technology

Autonomous vehicles or self-driving cars are revolutionizing the transportation industry by enhancing efficiency, safety, and convenience. However, the autonomous vehicle technology also introduces new risks that has to be considered and managed carefully. The major risks of autonomous vehicle technology are related to safety and cybersecurity.

In this chapter, we will learn about the main risks associated with the autonomous vehicle technology.

Safety Risks

Autonomous vehicles are completely dependent on sophisticated technology, sensors, camera, radar, and artificial intelligence for their self-driving operation. A failure in any of these components can lead to dangerous situations like unable to detect obstacles, road signs, traffic signals, pedestrians, etc. This situation can pose serious safety risks.

The following are some examples of safety risks associated with autonomous vehicles −

• Sensor Malfunctioning − Autonomous vehicles use a wide range of advanced sensors like LiDAR, RADAR, cameras, etc. to monitor and understand their surroundings. If any of these sensors fail or malfunction, then the vehicle may become unable to identify specific objects and it can make incorrect decisions.
• Unexpected Autopilot Function − Sometimes, autonomous vehicles can perform unexpected autopilot function that can lead to dangerous situations, if the driver is not ready to take the control.
• Misinterpretation of Traffic Pattern − Autonomous vehicles use traffic pattern recognition to safely navigate the vehicle on roads and make driving decisions. But they can sometimes misinterpret the traffic pattern due to temporary changes in the infrastructure and can pose safety risks.

All these safety risks in autonomous vehicles can be minimized through −

• Continuous testing and upgradation of autonomous driving algorithms.
• Implementing rigorous safety standards.
• Developing fail-safe systems to stop autonomous vehicles safely, if errors occur.

Risks of Cybersecurity Threats

Autonomous vehicles are connected to communication networks, other vehicles, cloud servers, and infrastructure components. Also, these vehicles use a huge amount of data to perform driving autonomously. This data and connectivity make the autonomous vehicles vulnerable to cyber threats and hacking. Cyber criminals may attempt to hack these vehicles to get access to its controls and data.

Some of the potential cybersecurity risks associated with the autonomous vehicles are explained below −

• Hijacking − Hackers can try to access and control the critical systems of an autonomous vehicle. They can perform unsafe braking, acceleration, and steering to put the passengers and other vehicles or pedestrians on the roads at a serious risk.
• Sensitive Data Theft − Autonomous vehicles collect and store sensitive data like location history, user preferences, driving habits, traffic rules, etc. Cyber criminals can steal this data and use it for various purposes like identity theft, plan illegal activities, track individuals, and more.
• Vehicle-to-Everything Communication Disruption − As we know, vehicle-to-everything communication is the backbone of safe and reliable operation of autonomous vehicles. It involves the communication of autonomous vehicles with other vehicles, cloud servers, and infrastructure components. Cyber criminals can disrupt this communication and connectivity that can lead to confusion on roads and may cause unsafe driving behavior and serious accidents.

We can follow the practices given below to secure the autonomous vehicles from cybersecurity threats −

• Implementing multiple layers of security like encryption, authentication, etc.
• Regularly updating software.
• Developing resilient cybersecurity systems against potential risks.

Radiation Exposure Risks

Autonomous vehicles use a large number of sensors and other electronic devices, all these components emit a significant amount of electromagnetic radiation for communication and connectivity with each other. However, electromagnetic radiation is considered safe below a certain level, but a prolonged exposure to it can pose many health concerns to passengers.

The following are some common health risks associated with autonomous vehicles due to electromagnetic radiation −

• Headache and Migraine − A prolonged exposure to electromagnetic radiation emitted by autonomous vehicle can cause headache and migraine to some individuals.
• Sleep Disorder − An extended exposure to electromagnetic radiation can also impact the sleep cycle and may cause insomnia.
• Long-Term Effects − According to some studies, it is suggested that an extended exposure to electromagnetic radiation can cause issues like anxiety, fatigue, cognitive disability, etc.

Autonomous vehicle manufacturers could try to minimize the radiation exposure by following these practices −

• Introducing shielding technologies
• Regulating sources of electromagnetic radiation
• Spreading awareness of radiation exposure risks
• Adopting safe vehicle designs
• Investing in studies and testing for long-term impacts of radiation on health, etc.

Battery Failure Risks

Autonomous vehicles are dependent on rechargeable batteries for receiving power. Most autonomous vehicles use lithium-ion batteries, which are quite powerful and reliable due to mature technology. However, if these batteries get damaged by any reason, then they become unstable. These batteries are also subjected to risk of thermal runway, which is an undesirable condition in which the battery starts releasing uncontrollable energy due to overheating. This kind of battery failures can cause fires and explosions in the vehicle.

Some of the key battery-related risks associated with autonomous vehicles are listed here −

• Fire and Explosion Hazards − Damaged batteries are prone to catch fires, which are highly challenging to control and may cause explosions. These battery explosions can also send broken parts from the vehicle at high speeds, which can injure nearby people.
• Emission of Toxic Gases − Overheated li-ion batteries release toxic gases like carbon monoxide, hydrogen fluoride, etc. These gases can pose serious health issues to passengers travelling in autonomous vehicles.

The following are some practices that automakers can follow to address battery-related risks in autonomous vehicles −

• Prepare safe battery designs
• Provide proper cooling systems to reduce the risk of thermal runaway
• Use fire-resistant material for making batteries, etc.

Hardware Failure Risks

In autonomous vehicles, the risks are sometimes posed due to failure of hardware components like braking system, steering control, radar, LiDAR, etc. These failures make the vehicle unable to navigate and respond to the surroundings and obstacles. They can lead to collisions with other vehicles and accidents.

To reduce hardware failure risks, the vehicles must equip with robust hardware components and go under regular maintenance checks. Systems like real-time diagnostic tools can also be integrated into the autonomous vehicles to detect and respond to hardware related issues before their actual failure.

Ethical Guidance Risks

Autonomous vehicles are dependent on artificial intelligence for making decisions. It arises a question that how can the vehicle make right decisions to ensure public safety and fairness, where human judgement is required.

For example, consider an unavoidable road accident condition. Now, in this situation, how will the autonomous vehicle decide whom to protect first between its passengers or other road users.

Conclusion

In conclusion, autonomous vehicles must be engineered well to avoid all these risks and issues to make the autonomous driving safer and meet the regulatory standards. Lets move to the next chapter and learn about the key Applications of Autonomous Vehicles.

Autonomous Vehicles - Applications

Autonomous vehicles or self-driving cars are playing a vital role across various industries with their capability to drive without any human intervention. These days, autonomous vehicles are using advanced sensor technologies, artificial intelligence, and connectivity. These technologies making autonomous vehicles an invaluable resource across multiple industries, ranging from urban mobility to mining.

Read this chapter to learn the applications of autonomous vehicles and understand how they are transforming different industries, enhancing efficiency and safety in transportation.

Autonomous Vehicles in Mobility

Autonomous vehicles have made significant development in the mobility sector, especially in urban areas. These vehicles are minimizing human errors in driving and enhancing the road safety. Autonomous vehicles are reducing the driving uncertainty and hence reducing the changes of accidents.

With increasing customer demand for safer, faster, and efficient mobility option, many mobility companies are upgrading existing vehicles and designing new autonomous vehicles like self-driving cars and buses that can effectively operate in urban environment.

Autonomous Vehicles in Smart Cities

Autonomous vehicles are also being used in smart cities to automate various services such as public transit, waste collection, emergency response, etc.

In smart cities, autonomous vehicles are playing a vital role by reducing the traffic congestion on roads, enhancing road safety, providing better traffic management, improving the operational efficiency, etc. All these activities are crucial for better city planning.

Autonomous Vehicles in Logistics

In logistics industry, there are several challenges such as labor shortage, very high fuel cost, operational inefficiencies, etc.

Autonomous vehicles like sidewalk delivery system, on-road vans, and autonomous trucks help in reducing all these issues. Therefore, autonomous vehicles provide several benefits to companies, such as supply chain automation, minimized emissions, increased lane capacity, reduced maintenance cost, etc.

Autonomous Vehicles in Manufacturing

Autonomous vehicles are also improving the operations in manufacturing industries. They are providing facilities like automated material handling and transportation, which streamline the workflows.

In manufacturing industries, autonomous vehicles are also being engaged in automating tasks like lifting, loading, and transporting materials, which are allowing manufacturers to scale their operations, meet the market demands, and reduce burden on supply chains.

Autonomous Vehicles in Healthcare

Autonomous vehicles are also reshaping the healthcare sector by providing enhanced emergency response and mobile healthcare facilities.

Autonomous vehicles help patients and disable persons to reach hospitals and access healthcare services. These vehicles are also allowing healthcare providers to deliver medical assistance efficiently.

Autonomous Vehicles in Agriculture

Autonomous vehicles are also advancing the agriculture industry by using IoT and operational technology systems to automate time consuming and effort-taking tasks like tilling, harvesting, and transporting materials. These vehicles are reducing labor costs involved and improving productivity.

Autonomous Vehicles in Aerospace

In aerospace industry, autonomous vehicle technology is making the air travel safer and efficient. This vehicle technology improving the safety of long-distance flights and reducing the associated risks. It is also reducing the operational costs and enhancing the customer satisfaction.

Autonomous Vehicles in Construction

Autonomous vehicles are also transforming the construction industry by replacing human labor in dangerous environments and automating tasks like dozing, hauling, carrying heavy loads, etc. These vehicles are making safe work environment, enhancing fuel efficiency, and reducing emissions.

Autonomous Vehicles in Mining

Autonomous vehicles are also being used in mining industries to allow continuous production with minimal errors, improved efficiency, enhanced safety, and reduced operational cost.

Conclusion

This is all about the applications of autonomous vehicles across various industries. Autonomous vehicles offer improved productivity, safety, and efficiency, with reduced costs involved. The use of autonomous vehicles is reshaping the mobility and transportation industry, and making them sustainable.