Autonomous vehicles, or “robocars,” have emerged in recent years as the next big innovation in transportation. With many companies pursuing commercialization of these smart vehicles, it is imperative to find ways to secure intellectual property rights in this new and fast-changing space via a strong intellectual property protection strategy.
Autonomous vehicles, the self-driving cars, have the potential to transform our society for the better. According to some predictions, these vehicles will eliminate accidents and make transportation more efficient. To ensure this transformation happens in a responsible way, developers of autonomous vehicles must be aware of legal developments surrounding patents and trade secret that may affect their ability to bring self-driving cars to market.
The Growth of Autonomous Vehicles Patents
Autonomous vehicle technology is a rapidly evolving field, and as such, there has been a significant increase in the number of patents being filed related to this technology in recent years. Some of the key areas in which patents are being filed include sensor technology, mapping and localization, decision making and control algorithms, and communication systems. Companies such as Google, Tesla, and Uber have been some of the most active filers of patents in this space. Additionally, there has also been a rise in the number of patents being filed by traditional car manufacturers such as Ford, General Motors, and BMW.
Carmakers are increasingly patenting autonomous vehicle technology in the United States as they look to prepare for a future without drivers. Most patents in the automated car industry revolves around AI patents as well as augmented reality technology, which is also used in metaverse patents.
After reviewing the most recent patent trends on the topic of autonomous and connected vehicles, it’s clear that automakers, designers, and software developers alike are working hard to bring self-driving cars to consumers as soon as possible.
But why is Innovation and Patenting on Autonomous Vehicles suddenly growing so fast?
The technology of autonomous vehicles interacting with its environment is well-known in the US due to the companies with large R&D investments like Google, Apple and Uber.
For one thing, America is a highly populated country with a lot of people and cars. And larger populations means more traffic and accidents, which leads to the need for an easier way out.
However, they’re being introduced at much slower speeds due to regulation issues. The US has been quick to adapt autonomous vehicles since they also help ease traffic congestion which makes it easier for people to move around on public transportation systems, but using AI tech without a driver is still not permitted.
The Trend in Autonomous Vehicle Patents
Autonomous vehicles will transform the car experience, bringing with them a whole new set of issues.
The first patent in autonomous vehicle field was filed by Sartre, but it was considered as a patented AI system that can drive on highways or limited-access roads by itself.
Because of scarcity of patents given by the US PC & has been a trend since the late 1990s, there is virtually no US patent applications for self-driving cars before 2006. In 2012, only 59 US patents were granted for autonomous vehicle related technologies.
Sensor fusion systems
Sensor fusion is one of the most important topics in autonomous vehicles. It has the potential to revolutionize transportation by providing real-time insights into objects, people, and obstacles around you.
A sensor fusion algorithm is a software or hardware process that combines inputs from various sensors to yield a more accurate model of your surroundings. While the concept may seem straightforward, achieving it is far from straightforward.
There are many different algorithms that are used to accomplish this feat. Each has its own strengths and limitations. Choosing the right method can improve the reliability of your system.
Several sensor fusion approaches have been studied in the literature. These include the Multi-Sensor Data Fusion (MSDF) framework, the Camera and Radar Fusion (CAF), and the High-Level fusion method. The Multi-Sensor Data Fusion framework is a combination of several processing chains and object detection processes.
Sensor fusion is important because it overcomes the shortcomings of individual sensors. For instance, the camera and radar fusion process can provide high-resolution images of the environment around you. This information can then be used to support intelligent actions.
Although it isn’t as simple as it sounds, sensor fusion is a big step forward. It provides the most accurate environmental model possible. However, the model is only as good as the algorithms used to process the data.
One of the most common sensors in today’s vehicles is the LiDAR, which uses 3D spinning technology to produce reliable perception in day and night. In addition to providing depth perception, it provides reliable detection and localization.
Another type of sensor fusion is feature-level fusion, which fuses low-dimensional features from multiple infrared sensors. It is also a good idea to include information from multiple sensors of the same type in the fusion process.
There are other methods to achieve sensor fusion, such as using external hardware to synchronize the devices. Other sensor fusion algorithms use a measurement model or motion model to calculate the appropriate data for fusing.
Sensor fusion is an essential part of most automated driving systems. With the increasing complexity of the driving task, each type of sensor cannot provide all the information needed to make safe and intelligent decisions.
Connected autonomous vehicles (CAVs) are vehicles that have the ability to work together with other vehicles and roadside infrastructure to complete a variety of tasks. They are also capable of taking feasible maneuvers towards various road hazards.
The potential benefits of CAVs include the reduction of traffic congestion, lowering the cost of fuel, and improved safety. However, their introduction requires a hefty investment in research and development. Until that point, they will require years of testing to become fully-operational.
The connected autonomous vehicle is a new type of automated vehicle that shares sensing and lateral communication capabilities with other vehicles. It has the ability to monitor and communicate with other cars, pedestrians, and roadside infrastructure. As a result, the system can improve safety, reduce congestion, and improve the capacity of freeways.
Autonomous vehicles use on-board sensors to perceive and map objects on the roadway. These vehicles can recognize and identify pedestrians and cyclists, detect obstructions, and follow other vehicles. In addition, they are expected to take safe maneuvering decisions in challenging driving situations.
A CRAV simulation, or augmented reality vehicle simulation, is a great way to visualize and test the capabilities of CAVs. A patented V2X Emulator developed by Tata Elsi replicates real-world conditions in a lab setting. This augmented reality system was awarded the silver medal by AutoSens in 2019, for its impressive validation tool.
The vehicle industry is moving toward automation, which offers many benefits, including traffic congestion reduction, reduced driver fatigue, and more efficient parking. To increase the safety of connected vehicles, it will be essential to refine road infrastructure and enable the proper data acquisition and data processing capabilities.
Research and development will also help to reduce the amount of time it takes for an AV to reach its destination. Some companies are already implementing Level 4 pilot projects.
Automated vehicles will eventually replace human drivers in many driving scenarios, but this process will take a while. In the meantime, researchers are evaluating their safety, reliability, and overall impact on transportation. Ultimately, there is a need to implement a robust communication protocol to keep all parties involved safe.
Truck platooning is an automated vehicle technology that uses V2V (vehicle to vehicle) communication to connect and control multiple trucks together. The truck platoon can increase throughput and reduce emissions by reducing aerodynamic drag.
Truck platooning is a potential solution to improve transportation in the U.S., which is already struggling with driver shortages. Platooning can help to reduce the number of accidents and improve fuel economy.
One company in Canada is developing an off-road truck platooning system. This will address transportation issues in areas with remote locations. Another research project in the U.S. includes Peterbilt.
According to the American Trucking Association, the primary goal of truck platooning is to make the road safer. While truck platooning can enhance safety, there are also hazards. Some of these hazards are caused by human error or hardware problems.
In addition, automated vehicles may not be as attentive to traffic as humans would be, leading to an increased risk of multiple-vehicle collisions. Automated vehicles can also cause longer highway trips and may cause drivers to stop more frequently to maintain traffic flow.
In order for the truck platooning industry to succeed, it must be accepted by fleets, regulators and the public. To accomplish this, initial testing and field testing will be needed. These tests will include determining how the system works, and assessing the safety and traffic flow.
Research on the impacts of truck platooning has included two heavy-duty platooning research projects funded by the Federal Highway Administration. Both of these studies used full-scale computational fluid dynamics simulations, which can be expensive. However, actual maneuvering of automated vehicles can provide accurate estimation of the effects of platooning.
An AI-based surrogate model can be used to optimize platoon configurations in near-real-time. It is possible to estimate the impact of platooning on traffic, road conditions, and freeway on-ramp areas.
One of the key issues is how to determine the best following distance for platoons. The platoon leader truck cannot give up the role unless a special case occurs. If the platoon is traveling at a high rate of speed, there is a greater benefit to platooning.
High-mobility autonomous systems
High-mobility autonomous systems are software-driven equipment that can navigate, carry passengers, and perform other tasks. They can also help to reduce traffic congestion. However, they raise a number of sociotechnical challenges. Among these are the need for efficient data management, data classification, and control methods. The deployment of these systems requires a range of tools to assess their impact on the transportation system.
One way of studying the effects of AMoD systems is to use an optimization-based approach. This includes determining how many AVs will be needed for a given service and then identifying the locations of charging stations. Some solutions include mathematical optimization, machine learning, or custom algorithms.
Another approach uses a central coordination scheme to optimize charging schedules and rebalancing. This allows a variety of vehicles to be guided simultaneously. It also minimizes externalities, allowing for better routing and rebalancing.
A number of researchers have investigated AMoD system deployment. Their work has led to a wide variety of approaches. These approaches generally use problem-specific structures and are not necessarily compatible with rigorous design of transportation infrastructure. Ultimately, a full characterization of an AMoD system requires additional properties, including trust and fairness, and the interactions between stakeholders in the mobility ecosystem.
In addition to reducing traffic congestion, new technologies could also reduce fuel consumption and help reduce crashes. But these solutions are not yet available. Until they are, current regulations will have a strong impact on future mobility outcomes.
For example, robo-taxis and ride-sharing require a robust policy framework that handles a number of issues, including the number of pickup and drop-off locations. The cost of these services can depend on the business case attractiveness of the fleet’s operation area and the buying power of customers. And while these services are still in their infancy, they could eventually become more price competitive with transit and taxi services.
Other examples of high-mobility autonomous systems include industrial inspection robots, rescue mission robots, and automated milking machines. These systems are used to perform heavy, repetitive tasks. While these types of robotic vehicles are not ready for everyday use, they can be used for special operations such as assisting surgeons in a high-precision procedure.
Issues Involved in Patenting Autonomous Vehicular Technology
In most states, the owner of an autonomous vehicle that is involved in a crash or other insurance-related incident has three options:
- accept liability for injuries or property damage caused by their vehicle;
- attempt to locate and sue the other driver for damages; and
- seek compensation from their insurer for any losses arising out of another driver’s fault.
But, the law relating to vehicular accidents by autonomous vehicles is still quite unclear.
As the number of autonomous vehicles on the road increases and crashes continue to occur, it is likely that there will be more disputes between drivers over who is responsible for paying damages, which may require these issues to be resolved by a court.