Throughout the years, researchers have worked on developing self driving cars. Since the 1950s, several promising trials have taken place. During the early days, research and development were done in the United States and Europe. However, there were still some issues that needed to be resolved. These include privacy issues, the use of GPS and inertial navigation systems, and a general Western bias towards autonomous vehicles.
Using network technology, autonomous vehicles are able to drive safely. This is achieved through rapid low-latency communications that address traffic management requirements. They can also process massive amounts of raw data.
The auto-driving car’s “smart” features are augmented by a variety of sensors and networking protocols. These are essential for efficient automated driving. They allow the vehicle to detect and react to hazardous situations and provide the driver with actionable information.
An autonomous network is programmable and can be configured to meet specific business needs. These include enabling real-world capabilities, maintaining network security and agility, and leveraging artificial intelligence. A key aspect of the ADN is its ability to deliver “hyper-loop” data from the network to device layers.
A typical autonomous system has five key functions. These include a high-precision driving system, the best route estimation and navigation, and the best way to perform a number of other functions.
Using AI, an autonomous network can perform tasks that a human operator could not afford. For example, an autonomous car may not know how to predict the behaviour of other road objects. It also has difficulties understanding things like the meaning of signals, such as a brake light.
GPS and inertial navigation system
Various navigation methods rely on external objects like landmarks, celestial bodies and other natural objects. Inertial navigation uses a combination of accelerometers, gyroscopes and other sensors to provide accurate data about the movement of an object.
Integrated inertial navigation systems are needed for self driving cars. These are small and light units that produce accurate and reliable measurements of the velocity and orientation of a vehicle. They are also used for stability control of other equipment that is installed in a driverless car.
Inertial measurement units contain angular and linear accelerometers as well as magnetometers. The IMU is typically placed in an orthogonal position so that all three axes can be accurately measured. Good performing MEMS IMUs deliver Gyro BI close to 1o/hr. and Angle Random Walk of 0.5o/hr.
Advanced driver assistance systems use IMUs in combination with vision sensors and GPS receivers. The information provided by the IMU is then filtered and incorporated into a central computing module to determine the position of the vehicle.
Various laws and regulations may apply to the data collection and usage of autonomous vehicles. The Federal Communications Act and the Electronic Communications Privacy Act protect against the unauthorized access of vehicle communications. The Fourth Amendment protects the privacy of individuals. There may be other relevant statutes governing the use of personal information.
The Federal Trade Commission (FTC) advises consumers to “take the time to read the ‘fine print’ in terms of data privacy, and make informed choices.” They recommend companies consider implementing safeguards to protect individual privacy. This includes an opt-in process to obtain consumers’ consent.
Although the Federal Communications Act and the Electronic Communications Privacy Act are likely to be the primary focuses of government and law enforcement, the legal issues associated with connected vehicles are likely to be handled by courts. Those tasked with ensuring safety and privacy in the automotive industry will have to weigh the benefits of their technology against the risks posed by external and internal security threats.
Western bias affects self-driving cars
Using a moral decision-making task, researchers presented two hypothetical scenarios and asked participants to select the best possible outcome. They then manipulated the task to see if it influenced the participants’ views on autonomous vehicles. After the manipulation check, participants completed a questionnaire addressing their attitudes on self-driving cars.
The study investigated how the cognitive limitations of human minds may affect moral decisions. Based on bounded rationality theory, a person’s emotional proximity to an event can cause moral bias. The results showed that participants were more likely to make utilitarian moral decisions when confronted with impersonal moral dilemmas. However, when faced with a personal dilemma, they were more likely to make deontological moral decisions.
This study was carried out with a diverse sample of Japanese and Chinese participants. The results showed that the attribution of responsibility to an AI was lower in Japan than China. In addition, Japanese participants chose an AI over a pedestrian-first scenario.