How artificial intelligence is taking robotaxis from science fiction to reality

In December 2022, Cruise Automation announced the launch of its fully driverless robotaxi service in Austin. Texas and Phoenix, Arizona. This is the first expansion beyond the robotaxi launch in San Francisco this summer by Cruise Automation. It plans to launch its fully driverless commercial service in more cities in 2023. This month, Uber also announced that it has launched its first robotaxi for commercial use in Las Vegas.

History of Cruise Automation

Cruise Automation was acquired by General Motors (GM) in 2016 for an undisclosed amount. The acquisition price was not disclosed, but the deal was rumored to be valued at more than $1 billion. The acquisition was made to accelerate GM’s efforts in the development of autonomous vehicles, as Cruise Automation was a leading player in the field of autonomous driving technology.

Cruise Automation is an autonomous car technology company that has been at the forefront of the development of robotaxis, or autonomous taxis. Founded in 2013, Cruise has made significant strides in the development and deployment of autonomous vehicles, with a focus on creating a robotaxis network that would revolutionize transportation as we know it.

Cruise Automation’s journey began with the development of an autonomous car kit, which was designed to retrofit existing vehicles with autonomous driving capabilities. This kit was intended to help companies and individuals test and evaluate the potential of self-driving cars, and it quickly gained traction in the market.

As Cruise’s technology matured, the company began exploring the possibility of building a fleet of self-driving cars that could be used as robotaxis. These robotaxis could pick up and drop off passengers without the need for a human driver, potentially revolutionizing the transportation industry.

In 2016, Cruise announced that he was working on a fleet of Chevrolet Bolt autonomous electric vehicles, to be used as robotaxis in the future. This was a major milestone for the company, as it marked the first time Cruise publicly revealed his plans to build a fleet of self-driving cars.

In 2020, Cruise made headlines when he announced that he had received a permit from the California Department of Motor Vehicles to operate his autonomous vehicles on public roads without a safety driver. This marked a significant milestone for the company, as it indicated that its technology was ready for deployment on public roads.

In November 2022, Cruise announced that he had driven nearly five million total miles, including 500,000 self-driving miles, without major incident.

History of robotaxis

The concept of robotaxis has been around for decades, with the first documented plans for autonomous taxis dating back to the 1960s. However, it was not until the 21st century that the technology and infrastructure necessary for the widespread deployment of robotaxis were made. reality.

One of the earliest and most influential proponents of autonomous taxis was the science fiction author Isaac Asimov. In his famous “Three Laws of Robotics”, Asimov proposed the idea of ​​robots that could be used as autonomous taxis, transporting people around cities without the need for human intervention. While Asimov’s vision was purely fictional at the time, it has served as a guiding principle for many researchers and engineers working on autonomous vehicles over the years.

The first practical experiments with autonomous taxis began in the 1980s and 1990s, with various research groups testing prototypes on public roads. These early efforts were limited in scope and often faced significant technical challenges, but they paved the way for the more advanced robotaxis to come later.

At the beginning of the 21st century, several companies began to develop autonomous vehicle technology with the goal of implementing a robotaxis on a commercial scale. These efforts have been aided by advances in sensors, computer vision, and artificial intelligence, which have made it possible for autonomous vehicles to navigate complex urban environments with a high degree of precision.

One of the first companies to implement a fleet of autonomous taxis was Waymo, a subsidiary of Google’s parent company Alphabet. Waymo began testing its autonomous taxis in Phoenix, Arizona, in 2016 and has since expanded its fleet to several other cities in the United States. Other companies, such as Uber and Lyft, have also tested autonomous taxis in select cities, although they have faced regulatory and technological challenges along the way. Tesla Motors debuted its semi-autonomous driving system, called Autopilot, in 2014. It employs an array of radar and ultrasonic sensors and cameras positioned around the car and essentially incorporates the car’s suite of driver assistance features to enable rudimentary autonomous driving. . on the freeway.

Despite these efforts, widespread deployment of robotaxis has yet to occur. Many experts believe it will be several more years before autonomous taxis are common on the roads, as there are still many technical and regulatory hurdles to overcome. However, the progress that has been made so far suggests that the robotaxis dream is closer than ever to becoming a reality.

Role of AI and data in Robotaxis

The role of data in robotaxis is to provide the information necessary for the vehicle to navigate safely and efficiently. This includes data on the location and design of roads, traffic patterns, and potential hazards such as pedestrians or other vehicles. It also includes data on vehicle performance and reliability, as well as data on driver preferences and behaviors. By analyzing and using this data, robotic taxis can optimize their routes, improve their safety and reliability, and provide a better overall experience for passengers.

One of the main ways that AI has contributed to autonomous driving, in general, is through its ability to process and interpret vast amounts of data. Autonomous vehicles are equipped with a wide range of sensors, such as lidar, radar, and cameras, which generate huge amounts of data that need to be analyzed in real time. This is where AI comes in, as it allows the vehicle to process and interpret this data, allowing it to make decisions about how to navigate the road and respond to different situations.

AI can power navigation and location of robotaxis. AI algorithms are used to enable robotaxis to navigate through different environments, including urban and suburban areas, and to pinpoint its location using a variety of sensors and mapping technology. AI algorithms are also used to enable robotaxis to make decisions in real time, such as determining the best route to take or how to respond to changing traffic conditions. AI algorithms are used to enable robotaxis to perceive and understand its environment, including identifying objects, pedestrians, and other vehicles and predicting their movements. AI algorithms are used to enable the robotaxis to communicate with other vehicles, infrastructure and passengers, using a variety of technologies such as V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communication.

AI has also been used to develop algorithms that allow autonomous vehicles to learn and adapt to their environment. These algorithms allow the vehicle to improve its performance over time as it collects more data and experiences different scenarios. This is particularly important in the development of autonomous driving, as it allows the vehicle to learn and adapt to new situations, such as changing road conditions, traffic patterns and weather conditions.

Another key role for AI in autonomous driving is the development of machine learning systems, which allow the vehicle to recognize and classify different objects and features in the environment. These systems use AI to analyze data from sensors and cameras and learn from this data, allowing the vehicle to identify and respond to different objects and features in the environment. This is crucial for autonomous driving, as it allows the vehicle to recognize and respond to other vehicles, pedestrians, traffic signs, and other road features.


Overall, AI has played a critical role in the development of robotaxis, enabling the development of advanced sensors, algorithms, and machine learning systems that enable these vehicles to navigate roads and respond to their environment. The use of AI in robotaxis allows them to operate safely and efficiently without the need for human intervention. In addition to Cruise Automation, Waymo, Tesla, Uber, and Lyft have played critical roles in the development of robotaxis, making significant strides over the past decade in the development and deployment of autonomous vehicles. With the rollout of robotaxis beyond San Francisco, it’s clear they’re at the forefront of a revolution that’s about to change the way we think about transportation.

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