The autonomous vehicle industry is progressing at a rapid speed and in just about two years, there could be 10 million self-driving cars in Europe. Self-driven cars are one of the most sought-after concepts by leading car manufacturers. However, giving control of your car to virtual technologies can be quite intimidating for a few.
For tech giants and corporations who rely on using transportation for business, autonomous vehicles not only promise higher efficiency but massive cost savings.
Imagine traveling in unmanned taxis that would take you to your destination, using the shortest route possible – sounds interesting right? Self-driven taxis are one of the many facades of the changes autonomous vehicles would bring.
The rise of self-driving cars and other vehicles will bring about a new age, one that is more efficient in every manner. Analysts predict a massive decline in the number of road accidents, which has one of the highest fatality rates in the world. Self-driving vehicles would be more disciplined to follow speed limits, finding the shortest route to a destination, and drive with increased efficiency that would lower the risks to passengers and passerby. However, what seems like a challenge at this age is relying on a car to make accurate and swift decisions that mimic the capabilities of humans, but only with greater precision – That is where Artificial Intelligence comes in.
How AI is driving the future of autonomous vehicles
One of the biggest challenges faced by autonomous vehicles is factoring all the information and data to make accurate decisions in the shortest span of time. On the road, drivers are required to make quick maneuvers, apply sudden brakes, and be varying of their surroundings. Even with humans, there is always a margin of error. However, there is a difference when you are in a manned vehicle and an autonomous vehicle.
If an unmanned vehicle gets into an accident, people would struggle to find the one at fault; in most cases, the manufacturing company will be blamed.
If you have ever traveled in a self-driven transport such as electric trains, you would notice a difference when you travel in an autonomous car. Unlike trains and trams, which have rails to steer their direction and operate under a controlled environment, self-driven cars would be a completely new experience. You can never predict the car movements. While it sounds intimidating, the AI systems make the vehicles more receptive to learning and making efficient decisions.
A vehicle moving on the road would need to factor for other moving objects such as other vehicles, humans, animals, birds, and other obstructions. A human eye can perceive the nature of the object at a glance despite the many varying features. For example, you can easily distinguish a car from a bus even if it has a different shape, size, and color. For computers, such perceptions are hard to determine. They can only identify objects that are defined in the database which calls for better systems that can adapt to their surroundings easily.
To tackle the problem, manufacturers are investing in modern AI tools that would take understand their surroundings just as we do and could comprehend various objects for the greater driving experience.
The best vehicles in the market would use three different technologies to get a comprehensive view of the surroundings that would be analyzed in a fraction of a second by onboard computers. For example, Nvidia introduced Pegasus, a Drive PX AI Platform that can process over 320 trillion operations in a second, which would redefine the onboard computing capabilities. With Pegasus, vehicles could process immense data at super speeds giving adequate time-response to critical scenarios, which require emergency braking and maneuvering.
The Data Challenge
Since self-driven vehicles would rely on gathering real-time data and calibrating the information with satellite systems to make efficient decisions. However, that can pose a challenge in future when millions of vehicles interact with data servers in real-time. The heavy load of data processing can delay the response time by the servers that leads to grave concern.
While some manufacturers are planning for a fully autonomous vehicle, others are developing models that would offer self-drive options as well with smart features that would monitor the drivers gaze to determine if they are able to drive the vehicle. Such features, along with real-time data gathering can pose a significant risk of data theft i.e. leaking out of personally identifiable information of the driver and other passengers.
According to Daryn Nakhuda, Founder and CEO of Mighty AI Autonomous vehicles collect massive amounts of real-life information. There is a need for automated blurring solutions and systems that can anonymize the individuals and protect their privacy.
Second, in a broader spectrum, self-driving vehicles would face multiple hacking risks, network delays, and system failures that can cause catastrophes. Imagine how it would be if several hundred vehicles were in control of a cyber-criminal, it would lead to a disaster. As technology progresses, so does the probability of greater cybercrimes. Since autonomous vehicles would rely on computing devices that would be connected to a network, it faces a risk of hacks that can give criminals remote access to the vehicles mainframe.
To mitigate these challenges, manufacturers and developers are posing a unique solution that would maintain two sets of computing devices onboard. The purpose of two onboard computing devices will allow the vehicle to act independently to its emergency response systems and proactively monitor its surroundings as well as store personal information of the driver.
The second system would interact with the satellite for real-time updates on traffic and communicate the information with the main server. Such implications would increase the information transmission rates and would mitigate the risks of delays from the server. There AI systems would act as two independent control systems that would complement and support the overall functions of the vehicle, but offer reinforced security and control over the vehicle.
This also limits the risks of cybercriminals from hacking into the systems and taking advantage of the vehicle as the primary system would be disconnected from the server.
AI Systems for Vehicle Maintenance
Modern AI systems would prevent breakdowns and lower maintenance problems by proactively monitoring the overall condition of the vehicle. These systems can schedule maintenance before a problem incurs using predictive maintenance analytics.
The smart systems are necessary for massive autonomous vehicles such as Tesla’s semi-electric trucks or UberFreight, as human intervention would be limited and vehicles would drive on their own. This feature is beneficial for large transportation companies involved in the supply chain, public transportation, etc.
