Waymo’s Self-Driving Cars: How They Work


The Sensors

The driving force behind the functionality of Waymo’s self-driving cars lies in their advanced sensor systems. These sensors serve as the eyes and ears of the vehicle, enabling it to perceive and comprehend its surroundings in real-time. By collecting and analyzing data from various sources, Waymo’s autonomous vehicles can make informed decisions and navigate safely on the roads.

There are several types of sensors utilized by Waymo’s self-driving cars:

  • The Lidar System: Lidar, short for Light Detection and Ranging, is a key sensor technology used by Waymo. It emits laser beams and measures the time it takes for the light to bounce back. This enables the car to create a 3D map of its surroundings, detecting objects, pedestrians, and other vehicles with incredible precision.
  • Cameras: Similar to human vision, cameras capture visual information from the vehicle’s perspective. Waymo’s self-driving cars are equipped with multiple cameras strategically placed to provide a comprehensive view of the environment. These cameras process real-time images to detect lane markings, traffic lights, and other critical visual cues for safe navigation.
  • Radar: Radar sensors use radio waves to detect objects and their proximity to the vehicle. They are particularly useful in low visibility scenarios, such as fog or heavy rain. By measuring the time it takes for the radio waves to bounce back from objects, radar sensors provide crucial distance and speed information.
  • GPS: The Global Positioning System (GPS) is an essential component of Waymo’s self-driving cars. It provides accurate location data, enabling the vehicle to determine its position on the map. Combined with other sensor inputs, the GPS enhances navigation capabilities and helps the vehicle adapt to changing road conditions.

These various sensors work harmoniously to gather a vast amount of data about the car’s surroundings. The sensors continuously generate a stream of information that is processed in real-time by the vehicle’s computer system, allowing it to interpret the environment and make informed decisions.

In the next section, we will explore how this wealth of data is utilized by Waymo’s self-driving car through the power of artificial intelligence algorithms.

The Lidar System

At the core of Waymo’s self-driving technology is the sophisticated Lidar system. Lidar, which stands for Light Detection and Ranging, plays a crucial role in enabling the vehicle to sense its surroundings with exceptional precision.

The Lidar system functions by emitting laser beams in different directions and measuring the time it takes for the reflected light to return. This allows the vehicle to construct a detailed and accurate 3D map of its environment. The emitted laser beams capture information about the distance, shape, and position of objects, ensuring that the self-driving car has a comprehensive understanding of its surroundings.

Waymo’s Lidar system utilizes a combination of mechanical and solid-state technology to achieve remarkable performance. It comprises a rotating platform that houses multiple laser emitters and sensors. As the platform spins, the laser beams are emitted and rapidly scanned across the environment, covering a wide range of angles. The sensors then capture the backscattered light and convert it into data that can be processed by the vehicle’s computer system.

One of the key advantages of Lidar technology is its ability to create a highly accurate and detailed 3D representation of the environment. By measuring the time it takes for the laser beams to bounce back, Lidar can precisely determine the distance between the vehicle and surrounding objects, enabling it to detect and classify pedestrians, cyclists, vehicles, and various other obstacles with remarkable accuracy.

Furthermore, the Lidar system provides the vehicle with a real-time understanding of the environment, allowing it to adapt to dynamic situations on the road. It can detect and track the movements of objects, predicting their future positions and enabling the self-driving car to make informed decisions in complex traffic scenarios.

Waymo’s continuous innovation in Lidar technology has led to significant advances in its self-driving capabilities. They have developed custom-designed Lidar sensors that are more compact, reliable, and cost-effective, allowing for widespread deployment on their fleet of autonomous vehicles.


Another crucial component of Waymo’s self-driving technology is the array of cameras equipped on their autonomous vehicles. These cameras serve as the visual perception system, providing a detailed view of the surrounding environment.

Waymo utilizes a combination of high-resolution and wide-angle cameras strategically placed on the vehicle to capture a comprehensive picture of its surroundings. These cameras work in harmony with other sensor inputs to provide a holistic understanding of the road conditions.

The primary purpose of the cameras is to detect and interpret visual cues, just like human drivers do. They capture real-time images of the environment, including lane markings, road signs, traffic lights, and the movement of objects and vehicles. By continuously analyzing these visual inputs, the self-driving car can navigate the roads safely.

The role of the cameras goes beyond mere object detection; they also play a vital role in object classification and tracking. By utilizing sophisticated computer vision algorithms, Waymo’s self-driving system can recognize and differentiate between different types of objects on the road, such as pedestrians, bicycles, and other vehicles. This information is then used to make informed decisions about the vehicle’s behavior.

Cameras, coupled with advanced image processing techniques, enable the self-driving car to have a real-time understanding of the road ahead. They can recognize complex scenarios, such as merging lanes, roundabouts, and pedestrian crossings, and respond accordingly. By continuously monitoring the visual inputs from the cameras, the vehicle can adapt to rapidly changing situations and ensure passenger safety.

Waymo’s cameras are equipped with features like high dynamic range (HDR) imaging, which enhances visibility in various lighting conditions. This allows the cameras to perform well even in challenging scenarios, such as bright sunlight or dimly lit streets.

Moreover, the data captured by the cameras is not just used for immediate decision-making but also for enhancing the overall self-driving system. Waymo leverages this data to train their artificial intelligence algorithms, making the system more intelligent and capable over time. By continuously improving the algorithms through machine learning, Waymo aims to deliver safer and more reliable autonomous driving technology.

In the next section, we will explore the role of radar sensors in Waymo’s self-driving cars.


Radar technology plays a vital role in Waymo’s self-driving cars, complementing the capabilities of the Lidar and camera systems. Radar sensors utilize radio waves to detect and measure the distance to objects in the vehicle’s vicinity, providing valuable information about the surrounding environment.

One of the key advantages of radar sensors is their ability to operate effectively in various weather conditions, such as fog, rain, or darkness, where other sensors may encounter limitations. Radar waves are less affected by atmospheric conditions and can penetrate certain obstacles, ensuring reliable object detection regardless of external factors.

The radar sensors on Waymo’s self-driving cars emit radio waves that bounce off surrounding objects, and the sensors then measure the time it takes for the waves to return. By analyzing the time delay and frequency shift of the returned waves, the system can calculate the distance, speed, and even relative motion of objects in the environment.

These radar measurements provide crucial data for adaptive cruise control, collision avoidance, and other important features of Waymo’s self-driving technology. By continuously monitoring the proximity of objects and their movements, the vehicle can make informed decisions to ensure the safety of passengers and other road users.

Moreover, radar sensors are particularly effective in situations where visual cues may be limited, such as detecting objects that are obstructed by other vehicles or buildings. This additional layer of perception enhances the overall robustness and reliability of Waymo’s self-driving system.

Waymo leverages a combination of long-range and short-range radar sensors to cover different distances and provide a comprehensive picture of the surroundings. Long-range radar sensors are responsible for detecting objects at a greater distance, while short-range radar sensors are used for close-range object detection, providing a detailed view of the immediate environment around the vehicle.

The data from the radar sensors is integrated with information from other sensor inputs, such as the Lidar and cameras, to create a holistic perception of the environment. This fusion of sensor data ensures that the self-driving car has a complete understanding of the surroundings, allowing it to make accurate decisions and navigate safely on the roads.

In the following section, we will explore the role of GPS in Waymo’s self-driving technology.


Global Positioning System (GPS) technology is an integral part of Waymo’s self-driving cars, providing precise location data and enhancing their navigation capabilities. GPS allows the vehicle to determine its position on the map and accurately plan its route.

Waymo’s self-driving cars utilize a network of GPS satellites orbiting the Earth to triangulate the vehicle’s position. The GPS receivers on board the vehicle receive signals from multiple satellites and calculate the precise latitude, longitude, and altitude of the car.

The GPS data is essential for various aspects of the autonomous driving system. It enables the vehicle to accurately follow a pre-determined route, ensuring that it stays on the correct path and reaches its destination. This is particularly useful when navigating complex road systems or unfamiliar areas.

In addition to providing location information, GPS also provides the vehicle with data about its speed and direction of travel. This data is crucial for the control system to make accurate decisions and adjust the vehicle’s behavior as needed.

GPS technology is not only used for route planning and navigation but also for real-time mapping and location-based services. Waymo’s self-driving cars continuously update their maps, utilizing GPS data to enhance the accuracy and detail of the maps. This allows the vehicles to have up-to-date information about road conditions, speed limits, and other relevant factors.

Furthermore, the GPS data is integrated with other sensor inputs, such as the Lidar, cameras, and radar, to create a comprehensive understanding of the environment. By combining the precise location data from GPS with the real-time perception of the surroundings, Waymo’s self-driving cars can adapt to changing road conditions and make informed decisions.

While GPS technology is highly reliable, it does have limitations, particularly in urban environments with tall buildings or areas with dense tree cover. In such scenarios, the accuracy of the GPS signal can be affected, and additional sensors and algorithms are used to overcome these challenges and ensure precise localization.

Overall, GPS technology is a critical component of Waymo’s self-driving system, providing accurate and reliable location data for navigation, mapping, and real-time decision-making.

In the next section, we will delve into the role of the computer system and artificial intelligence algorithms in driving Waymo’s self-driving cars.

The Computer

At the heart of Waymo’s self-driving cars is a powerful computer system that processes vast amounts of data in real-time, enabling the vehicle to make intelligent decisions and navigate autonomously. The computer acts as the brain of the autonomous vehicle, analyzing sensor inputs, running sophisticated algorithms, and controlling various components of the car.

Waymo’s computer system is designed to handle the complex computational tasks required for autonomous driving. It is equipped with high-performance processors, advanced memory systems, and efficient data storage capabilities. These components work together to process sensor data quickly and accurately, ensuring timely responses and efficient decision-making.

The computer system receives data from various sensors, including the Lidar, cameras, radar, and GPS. This data is then processed and fused together to create a comprehensive understanding of the environment. The computer analyzes the sensor inputs to detect objects, track their movements, and identify potential risks or hazards on the road.

In addition to sensor data processing, the computer system also runs sophisticated artificial intelligence (AI) algorithms. These algorithms enable the vehicle to perceive and interpret the environment, make predictions about the behavior of other road users, and plan its actions accordingly.

The AI algorithms are trained using vast amounts of data collected during extensive testing and simulations. This training helps the system to learn patterns, make accurate predictions, and adapt to various driving scenarios. Machine learning techniques are employed to continuously improve the system’s performance over time, allowing for safer and more efficient self-driving capabilities.

Furthermore, the computer system is responsible for controlling various components of the vehicle, including acceleration, braking, and steering. It receives input from the AI algorithms and executes the appropriate commands to ensure safe and smooth operation.

Waymo’s computer system is not only capable of processing data and making decisions in real-time, but it also performs extensive diagnostic checks to ensure the system’s reliability. It continuously monitors the health and functionality of all subsystems and sensors, providing feedback and alerts in case of any anomalies.

The computer system plays a critical role in the overall performance of Waymo’s self-driving cars. Its powerful processing capabilities, combined with advanced AI algorithms, enable the vehicle to navigate complex traffic situations, adapt to changing road conditions, and provide passengers with a safe and comfortable ride.

In the next section, we will explore the role of artificial intelligence algorithms in perception and decision-making of Waymo’s self-driving cars.

Artificial Intelligence Algorithms

Artificial intelligence (AI) algorithms play a crucial role in the perception and decision-making capabilities of Waymo’s self-driving cars. These advanced algorithms process the data collected from the vehicle’s sensors and enable it to interpret and understand its surroundings in real-time.

One of the primary uses of AI algorithms in autonomous driving is object detection and classification. By analyzing the sensor inputs, such as Lidar, cameras, and radar data, the algorithms can identify and track various objects on the road, including pedestrians, vehicles, cyclists, and obstacles. This information allows the vehicle to make informed decisions and navigate safely.

The AI algorithms not only detect objects but also classify them based on their behavior and potential risks. For example, they can differentiate between a stationary object, a moving vehicle in the same direction, or a pedestrian crossing the road. This level of understanding assists the vehicle in predicting the intentions and movements of other road users, leading to better decision-making.

Additionally, the AI algorithms enable the vehicle to interpret complex traffic scenarios and make appropriate decisions. These algorithms can analyze the behavior of multiple objects simultaneously, predicting their future trajectories and determining the safest course of action. For instance, the vehicle can determine when to yield, change lanes, or overtake another vehicle based on a comprehensive understanding of the surrounding traffic dynamics.

Waymo’s AI algorithms also incorporate machine learning techniques to continuously improve their performance. The algorithms are trained using extensive datasets that include various driving scenarios and conditions. By learning from these datasets, the system can improve its ability to recognize and respond to diverse situations, enhancing its overall safety and efficiency.

Besides perception, AI algorithms also play a crucial role in planning and decision-making. The algorithms consider multiple factors, such as traffic conditions, road rules, and passenger preferences, to generate optimal plans for the vehicle’s route and behavior. This ensures a smooth and efficient driving experience for the passengers.

Moreover, the AI algorithms enable the vehicle to adapt to dynamic situations and unforeseen circumstances. The algorithms are designed to handle unpredictable events on the road, such as sudden lane changes, roadblocks, or emergency vehicles, by quickly evaluating the situation and responding accordingly. This adaptive capability enhances the vehicle’s safety and ensures a prompt reaction to unexpected events.

Overall, the use of artificial intelligence algorithms is fundamental in driving Waymo’s self-driving cars. These algorithms enable the vehicle to perceive and understand its environment, make informed decisions, and navigate autonomously. Through continuous learning and improvement, the AI algorithms pave the way for safer and more efficient autonomous driving technology.

In the next section, we will explore how Waymo’s self-driving cars map and understand their environment.

Mapping the Environment

Mapping the environment is a crucial aspect of Waymo’s self-driving technology. Before the vehicle hits the road, extensive mapping and data collection processes take place to create a detailed and accurate map of the operating area.

Waymo’s self-driving cars rely on high-definition maps that provide essential information about the roads, lane markings, traffic signs, and other relevant details. These maps serve as a valuable reference for the vehicle’s navigation and decision-making processes.

The mapping process begins with Waymo’s specially equipped vehicles, equipped with advanced sensors, driving through a particular area to capture detailed data about the surroundings. These sensors include Lidar, cameras, and GPS to accurately record the geometry and features of the environment.

As the sensors capture data, it is meticulously processed and analyzed to construct a precise and comprehensive map. The Lidar sensors measure the distance to objects, creating a 3D representation of the surroundings. Cameras capture visual information such as lane markings, road signs, and landmarks. GPS data is used to accurately geolocate the captured data within the map.

This mapping process is not a one-time event but an ongoing effort to ensure maps are up-to-date with changes in the environment. Waymo’s self-driving cars continuously contribute to map updates by collecting data during their autonomous drives. The collected data is cross-referenced with existing maps to update road conditions, new construction areas, and changes to lane markings.

Waymo’s high-definition maps provide additional information beyond what the vehicle’s sensors can perceive in real-time. This rich data enhances situational awareness and adds an extra layer of redundancy to the vehicle’s perception capabilities.

During autonomous operation, the vehicle’s sensors, including cameras, Lidar, and radar, continuously perceive the surrounding environment. This real-time data collected by the sensors is compared and aligned with the pre-existing high-definition map data to ensure accurate localization and precise understanding of the vehicle’s position within the mapped area.

The combination of real-time sensor data and high-definition maps enables the vehicle to make more informed decisions. For example, the vehicle can compare its sensor inputs with map data to identify upcoming curves, road intersections, or traffic signal locations, allowing it to plan its actions in advance.

Mapping the environment provides Waymo’s self-driving cars with a foundational understanding of the world around them. The combination of real-time sensor data and up-to-date high-definition maps empowers the vehicles to navigate complex road infrastructure and respond to dynamic traffic scenarios with confidence.

In the next section, we will explore how Waymo’s self-driving cars detect and track objects in their environment.

Object Detection and Tracking

Object detection and tracking are critical components of Waymo’s self-driving technology, enabling the vehicle to perceive and understand its surrounding environment. By accurately detecting and tracking objects, the vehicle can navigate safely and make informed decisions on the road.

Waymo’s self-driving cars utilize a combination of sensors, including Lidar, cameras, and radar, to detect and identify various objects in their vicinity. These sensors provide different types of data that, when fused together, create a comprehensive understanding of the objects and their movements.

Lidar sensors play a crucial role in object detection. By emitting laser beams and measuring the time it takes for the light to bounce back, Lidar sensors construct a detailed 3D map of the environment. This allows the vehicle to detect and recognize various objects, such as pedestrians, cyclists, vehicles, and obstacles, with high accuracy and precision.

Cameras also play a significant role in object detection. They capture visual information from the vehicle’s perspective, allowing the system to analyze and interpret the surroundings. By leveraging computer vision techniques and AI algorithms, Waymo’s self-driving cars can detect and classify objects based on their shape, size, and motion characteristics.

In addition, radar sensors contribute to object detection and tracking. They emit radio waves that bounce off surrounding objects, providing distance, speed, and relative motion information. This allows the vehicle to have a multi-layered perception of objects, incorporating data from Lidar, cameras, and radar for more reliable object detection and tracking.

Once objects are detected, Waymo’s self-driving cars employ sophisticated algorithms to track their movements. By continuously analyzing the sensor data, the system can predict the trajectories of objects, their changes in speed or direction, and potential interactions with the vehicle’s path.

Achieving accurate object detection and tracking is challenging due to the dynamic nature of the surrounding environment. Moving objects, such as pedestrians and vehicles, can change their trajectories or speed unexpectedly. Waymo’s self-driving cars are equipped with powerful processing capabilities and AI algorithms that can adapt to these dynamic situations, ensuring reliable tracking and accurate predictions.

The ability to detect and track objects in real-time enables Waymo’s self-driving cars to make informed decisions on the road. For example, the system can anticipate the movements of pedestrians at intersections, predict the behavior of other vehicles, and adjust its driving behavior accordingly to ensure safe and efficient navigation.

Object detection and tracking are fundamental components of Waymo’s self-driving technology, contributing to the vehicle’s perception capabilities and enabling it to navigate complex traffic scenarios with confidence.

In the next section, we will explore the decision-making and planning processes employed by Waymo’s self-driving cars.

Decision Making and Planning

Waymo’s self-driving cars rely on advanced decision-making and planning processes to navigate the roads safely and efficiently. These processes involve analyzing the vehicle’s surrounding environment, understanding the behavior of other road users, and making informed decisions based on this information.

Upon perceiving objects and obstacles in the environment through sensors like Lidar, cameras, and radar, Waymo’s self-driving cars employ sophisticated algorithms to assess potential risks and plan their actions accordingly.

One key aspect of decision making is predicting the behavior of other road users. By analyzing the movements and patterns of vehicles, pedestrians, and cyclists, the vehicle can anticipate their future actions. This predictive capability allows the self-driving car to make proactive decisions, such as adjusting speed, changing lanes, or yielding to ensure the safety of all road users.

Planning is another critical component of the self-driving system. Based on the information gathered from the environment, the vehicle’s current position, and its destination, the system generates a plan that outlines the actions the vehicle will take. This plan considers factors like traffic rules, road conditions, and input from the AI algorithms.

Waymo’s self-driving cars employ intelligent algorithms to evaluate the various options available at any given moment. These algorithms consider factors such as the vehicle’s intention, the behavior of other road users, the vehicle’s own trajectory, and the safety of all parties involved. By analyzing these factors, the system selects the most suitable course of action to ensure safe and efficient navigation.

The decision-making and planning processes occur in real-time, allowing the self-driving car to adapt to rapidly changing traffic scenarios. For example, the system employs defensive driving techniques to anticipate and respond to aggressive maneuvers from other vehicles, ensuring a safe and smooth driving experience.

Waymo’s self-driving cars continuously analyze sensor data to validate and update their decisions. Real-time inputs from the vehicle’s sensors, such as Lidar, cameras, and radar, inform the decision-making algorithms and help refine the planning process. This constant feedback loop ensures that the vehicle remains aware of its surroundings and makes adjustments as needed.

Furthermore, the decision-making and planning processes take into account passenger comfort and preferences. The self-driving car aims to provide a smooth and pleasant ride by considering factors such as acceleration, braking, and cornering forces. This attention to passenger comfort enhances the overall experience and instills confidence in autonomous technology.

In summary, the decision-making and planning processes in Waymo’s self-driving cars combine data analysis, predictive algorithms, and real-time adaptability to navigate the road safely and efficiently. By considering various factors and prioritizing safety, the system makes informed decisions and plans the vehicle’s actions in a dynamic and ever-changing environment.

In the next section, we will explore the control system that enables Waymo’s self-driving cars to execute their planned actions.

Control System

The control system in Waymo’s self-driving cars is responsible for executing the planned actions generated by the decision-making and planning processes. It translates the high-level decisions into specific commands that control the vehicle’s acceleration, braking, and steering, ensuring safe and precise movements on the road.

The control system consists of various components working together to operate the vehicle smoothly and efficiently. It receives input from sensors, such as Lidar, cameras, radar, and GPS, and uses this information to calculate the necessary adjustments required to follow the planned trajectory.

One essential aspect of the control system is the actuator interfaces, which interface with the physical components of the vehicle, such as the throttle, brakes, and steering system. These interfaces convert the computed commands from the control system into physical actions, controlling the vehicle’s speed, deceleration, and steering angle.

The control system also incorporates real-time feedback from the vehicle’s sensors to ensure accurate execution of the planned actions. By continuously monitoring the vehicle’s position, speed, and other factors, the control system can make necessary adjustments to maintain stability and safety.

Furthermore, the control system takes into account constraints and limitations imposed by the vehicle and the surrounding environment. For example, it considers the traction capabilities of the tires and the maximum braking force available to ensure optimal control during acceleration and deceleration.

The control system operates with an emphasis on safety, applying techniques like redundancy and fail-safe mechanisms. Redundancy ensures that critical control functions have backup systems in case of component failure, allowing the vehicle to continue operating safely. Fail-safe mechanisms monitor the performance of the control system and take appropriate actions in the event of any anomalies or malfunctions.

In addition to executing planned actions, the control system adjusts the vehicle’s behavior in response to real-time events. For example, it can respond to sudden obstacles or unexpected changes in traffic conditions by quickly adapting the vehicle’s speed or trajectory.

Waymo’s self-driving cars continuously optimize their control system based on real-world feedback and data. This iterative process helps fine-tune the control algorithms, enhancing the vehicle’s driving performance, and ensuring smooth operation.

The integration of the control system with the decision-making and planning processes allows Waymo’s self-driving cars to perform complex maneuvers, such as lane changes, merging, and maintaining a safe distance from other vehicles.

Through the coordination of various components and algorithms, the control system enables precise and safe execution of the planned actions, ensuring that Waymo’s self-driving cars navigate the roads confidently and reliably.

In the next section, we will explore the rigorous testing and safety measures implemented by Waymo to ensure the reliability and robustness of their self-driving technology.

Testing and Safety Measures

Waymo takes a rigorous approach to testing and implementing safety measures in the development and deployment of their self-driving technology. The company places paramount importance on ensuring the reliability, robustness, and safety of their autonomous vehicles.

Before Waymo’s self-driving cars are deployed on public roads, they undergo extensive testing in various conditions and scenarios. The vehicles go through a series of rigorous tests that simulate real-world environments, including urban settings, highways, and complex traffic scenarios.

Real-world testing is supplemented by virtual simulations, where the vehicles’ sensors and algorithms are put through numerous virtual scenarios. This allows Waymo to evaluate and refine the system in a controlled and safe environment, enabling continuous improvement and validation.

Waymo’s commitment to safety is demonstrated through their Safety Drivers, who accompany each self-driving vehicle during testing on public roads. These Safety Drivers are highly trained and have the ability to take over control of the vehicle if necessary, ensuring a safe driving experience during testing phases.

In order to continuously improve the safety and reliability of their self-driving technology, Waymo also employs extensive data collection and analysis. Each self-driving car collects a vast amount of data during its operations, allowing Waymo’s engineers to analyze and gain insights into various driving scenarios.

Data collected from the vehicles’ sensors, combined with detailed mapping information, is used to train and evolve AI algorithms. This iterative process helps improve the accuracy, responsiveness, and performance of the self-driving technology, further enhancing safety measures.

Furthermore, Waymo places a strong emphasis on transparency and collaboration in the development of their technology. The company actively seeks feedback from experts, regulators, and the public to improve their self-driving technology and ensure that the deployment is done responsibly and safely.

Waymo has gone to great lengths to create and implement a comprehensive safety framework for their self-driving cars. Their approach encompasses multiple layers of redundancy, monitoring, and fail-safe mechanisms to minimize risks and ensure the safety of passengers and other road users.

As part of their commitment to safety, Waymo’s self-driving cars are designed to comply with all applicable regulations and traffic laws. They adhere to strict quality control processes and safety guidelines to maintain a high standard of performance and reliability.

The extensive testing and safety measures implemented by Waymo reflect their dedication to responsible deployment and continuous improvement of self-driving technology. By prioritizing safety at every stage of development, Waymo aims to build trust in autonomous vehicles and provide safe, efficient, and sustainable transportation for the future.

In the final section, we will wrap up with a brief summary of the key points discussed throughout the article.