How AI is Driving the Autonomous Vehicle Revolution
Autonomous vehicles are self-driving cars designed to operate without human intervention by using Artificial Intelligence (AI). Using a large array of technical components including cameras, radars, and LIDAR, autonomous vehicles have the capability of replacing human drivers, acting as the brain and pilot of their own machinery.
By incorporating Deep Learning, autonomous vehicles can optimize travel routes and avoid collisions with other vehicles or humans, making them increasingly roadworthy; and as innovation continues to develop, we can expect even more advanced algorithms like Swarm Intelligence to change our entire road infrastructure.
Background of Autonomous Vehicles
Autonomous vehicles have been a concept for decades since radio-controlled cars first appeared at exhibitions in the early 1920s when the United States was experiencing major economic success during the post-WWI era. However, automobiles were still in their infancy.
It wasn’t until a half-century later, in the 1980s, that practical developments in AI made self-driving vehicles a realistic endeavor at Carnegie Mellon University and Bundeswehr University Munich, leading to DARPA’s Grand Challenge that hosted autonomous vehicles from across the globe at the turn of the 21st century.
Since then, some of the world’s largest companies such as Google and Tesla have successfully begun testing and developing autonomous vehicles, fusing them with more advanced AI systems that incorporate neural networks for safer, more general use.
How AI is Driving the Autonomous Vehicle Revolution
AI’s primary use in autonomous driving is to serve as the “brain” of the vehicle. By using computer vision, the vehicle can collect and interpret real-time data about road conditions for processing.
The objective of these systems is to identify and analyze crucial aspects of driving that all drivers must pay attention to including:
Navigation and Mapping: AI is necessary for understanding where a vehicle needs to go by interpreting GPS data and other information gathered from cameras and sensors. AI can create 3D visual maps with this data using a technique called Simultaneous Localization and Mapping (SLAM).
Decision Making: AI can make real-time judgments to determine safe driving speeds and when to switch lanes. This is incredibly important when considering the safety of fellow drivers.
Object Detection and Avoidance: Computer Vision is essential for autonomous driving, helping them avoid collisions and staying within their lanes. Convolution networks can also help autonomous vehicles interpret road signs and other important notices.
Predictive Maintenance: Like all vehicles, self-driving cars will still need regular maintenance and check-ups which AI can assist with by using Predictive Analytics to determine the optimal times for specific maintenance routines.
Challenges for Roadworthy Autonomous Vehicles
While autonomous vehicles already exist, many drivers still do not trust AI enough for public roads. Complex driving conditions can be hard to predict and pose a lot of risks to other vehicles when sensor data becomes hard to interpret.
Predicting human behavior from other drivers can also be a difficult task for some AI systems because drivers can often drive erratically either from being impatient, inebriated, or susceptible to road rage.
Companies like Tesla are already facing a number of legal challenges preventing them from offering fully autonomous driving to customers because of the lack of public trust and a need for more certainty that self-driving cars are as safe as fellow drivers (even if human drivers are statistically more dangerous).
The Future of AI in Autonomous Vehicles
Despite the challenges, autonomous vehicles pose incredible potential using swarm intelligence, which is a subfield of AI that attempts to mimic natural behaviors observed in animals and insects such as hive colonies and flock patterns.
By applying swarm intelligence to self-driving vehicles en masse, the future of traffic could reach new levels of efficiency that are unheard of. Establishing a network intelligence across all vehicles in a city could reduce driving times and rush hour congestion by allowing cars to work more in unison through hive-like communications.
While this reduces the need for human driving experience, forcing some individuals to adopt unwanted levels of dependency on expensive technology, it showcases how AI could revolutionize our roads and even apply to public transportation where benefits to the public would be exponential.
