Self-Driving Vehicle

Fully autonomous cars combining cameras and sensors that are in constant contact with the cloud which would replace current human-driven vehicles.
Technology Life Cycle

Technology Life Cycle


Marked by a rapid increase in technology adoption and market expansion. Innovations are refined, production costs decrease, and the technology gains widespread acceptance and use.

Technology Readiness Level (TRL)

Technology Readiness Level (TRL)

Prototype Demonstration

Prototype is fully demonstrated in operational environment.

Technology Diffusion

Technology Diffusion

Early Majority

Adopts technologies once they are proven by Early Adopters. They prefer technologies that are well established and reliable.

Self-Driving Vehicle

Fleets of autonomous cars are aiming to disrupt the urban mobility industry by removing the human factor. These vehicles use a combination of sensors, software, and machine learning algorithms to sense their environment, make decisions, and control their movements, such as performing maneuvers, lane following, automated parking, reacting to unpredictable road and traffic conditions, and complex end-to-end navigation.

The technology behind self-driving vehicles is complex and includes a wide range of sensors, such as cameras, LiDAR (Light Detection and Ranging), radar, and GPS, that collect data about the vehicle's surroundings. This data is then processed by software that uses machine learning algorithms to interpret the data and make decisions about how the vehicle should respond.

By incorporating real-time information from the cloud and utilizing vehicle-to-vehicle communication, routes could be optimized, with the capacity of vehicles better served, traffic lights eliminated, and overall transportation would become much safer. Human drivers confront and usually have to manage an incredible variety of incidents within their contexts, including geographic areas, road types, and traffic and weather conditions. Furthermore, drivers routinely face ethical decisions in assessing conflicting situations, for instance, whether to prioritize driver safety above the safety of other vehicles. Fully automated vehicles must take these scenarios into account as well, but the principles might vary according to the implemented algorithms.

Despite the downsides, one of the biggest challenges for modern society is public transportation, which is desperately demanding to be overhauled to benefit the whole worldwide community. Self-driving shuttles and buses could become potential improvements for public transit because they move numerous people at once, consequently improving mobility in cities in a way that would be even more efficient than self-driving cars.

Future Perspectives

The robotization of vehicles might drastically eliminate accidents due to driver error while maximizing vehicle utilization. A self-driving taxi could operate 24/7, thus reducing the number of vehicles needed for future urban demand while keeping prices accessible. Concerns over liability in case of accidents and other insurance-related issues remain unregulated and could delay implementation.

Although transportation-as-a-service is already starting to become a reality, networks of self-driving cars could completely eliminate the need for vehicle ownership. Safer road systems could cause radical redesigns of vehicle exteriors, possibly making them modular and attachable to one another, temporarily creating larger vehicles or the ability to add extra storage space on the fly. As a result, traffic lights, traffic police, driver's licenses, and drunk driving offenses could become a thing of the past. The comfort of driving could increase drastically with onboard massage services and beds for sleeping. This could also create opportunities for meaningful in-transit real and virtual meetings.

As algorithms optimize, there would be no excuses for being late. People would know when they must leave and when they would arrive. On the other hand, vehicle hacking could become a serious issue with always-connected vehicles and their multiple cameras and sensors.

Image generated by Envisioning using Midjourney

Self-Driving taxis may face unexpected problems.
FT article about a joint venture between Ford and Lyft.
This paper reviews the key technology of a self-driving car. In this paper, the four key technologies in self-driving car, namely, car navigation system, path planning, environment perception and car control, are addressed and surveyed. The main research institutions and groups in different countries are summarized. Finally, the debates of self-driving car are discussed and the development trend of self-driving car is predicted.
The implications of autonomous vehicles are vast, complex and difficult to predict. One thing is certain — their impact will be broad and significant.
Techcrunch video about Uber test with autonomous vehicles.
Arbe robotics is a leading firm in the sensoring and autonomous vehicles industry.
Autonomous vehicles (AVs) are the alternative solution for mobility in the future. The first major benefit of the self-driving cars would be the safety. Human errors cause most of the car incidents. Self-driving cars could improve public transportation services and decrease auto ownership because personal car won’t be necessary anymore. Self-driving car can be a solution to reduce the emissions of carbon dioxide (CO2). There are also some challenges to overcome: self-driving cars cannot operate very good in bad weather conditions and the legislative structure needs to be defined in case of any incident. This article presents the current status of this technology and what are the challenges of implementing intelligent systems that allow the handling of a vehicle without human intervention.
Coopetition is still a relatively new perspective and paradigm for considering relationships between networks, firms and organizations, and business units. The literature on coopetition focuses on developing several alternative perspectives of coopetition. Integrating theories on coopetition is an essential challenge for scholars of management and marketing. However, one possibility to challenge the contemporary field of coopetition is to introduce new topical themes of business and society and test their relationships with coopetition perspectives. The authors consider one technical disruption—self-driving cars—and its collaboration networks related to coopetition perspectives. Outcomes show the importance of lead users of this disruptive technology. Furthermore, coopetition, and especially competitive networks, seems to be an important strategy for developing new disruptive technologies according to the needs of markets.
The race to fully autonomous vehicles is on. In April, Elon Musk declared that Tesla should have over a million level 5 autonomous vehicles manufactured by 2020. To clarify, that means over a million cars equipped with the necessary hardware capable of driving with no help from a driver. That’s contingent, of course, on the software being ready. In addition, government approvals will be necessary (read: mandatory) long before self-driving Teslas will be commonplace.
By 2035, autonomous driving could create $300 billion to $400 billion in revenue. New research reveals what’s needed to win in the fast-changing passenger car market.
Companies are already testing systems like collaborative parking, remote-control summoning, and special picker-upper robots.
At Voyage we recently shared the news of Homer, our first self-driving taxi. Homer is outfitted with a whole range of sensors to aid in understanding and navigating the world, key to which is LIDAR…
Contract is the first in the world between a city and an autonomous car manufacturer

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