AI in Autonomous Vehicle Navigation and Control

Self-driving cars are becoming more common in our digital world as tech-savvy drivers begin switching to autonomous vehicles because they are better for the environment and provide new features that traditional combustion vehicles do not offer. 

To navigate the road safely, these vehicles integrate advanced Artificial Intelligence (AI) technology that understands how to operate vehicles. These Deep Learning algorithms are adept at monitoring their surroundings, making real-time decision making, and controlling a vehicle’s speed and as they become more ubiquitous on the road, we must  understand how they navigate the road. 

Evolution of Vehicle Navigation

Before AI, drivers used other, more conventional means of traveling by car around their cities and neighborhoods. This included GPS systems and online maps that could guide drivers to their destinations. Sensors and rear-view cameras also began appearing on cars too, giving drivers a 360-scope view of their surroundings. 

However, this has largely changed with the advent of AI with its ability to process large quantities of complex data which is required for the large amount of real-time data inputs generated by daily traffic. This helps predictive analytics determine the best routes through specific parts of a city with factors like the time of day/week to avoid congestion during rush hour and other busy areas. 

These AI models can also be integrated into GPS systems that work in unison to provide drivers with the most updated road conditions like road work ahead or major collisions on a freeway, helping AI find alternate routes faster. These algorithms have also become capable of recognizing signs and pedestrians, including it into their decision-making process. 

AI and Decision Making

Decision-making is a crucial element of autonomous vehicle navigation because it is the system that prevents collisions and accidents from occuring. To keep these systems operational, self-driving cars have a myriad of cameras and sensors built into their chassis that inform the AI models about their surroundings. Data is then processed through AI algorithms to spot important patterns on the road like traffic lanes. 

However, autonomous vehicle AI systems also need to be responsive and quick to act because road conditions are unpredictable. Situations like other cars making sudden lane changes or other drivers that may be intoxicated are complex challenges for AI systems to predict and respond to. Fortunately, AI is adaptable and uses continuous learning to pick up on these types of occurrences, and with a reaction speed faster than any human, can react to dangerous conditions more quickly than ordinary drivers. 

Deep Learning and Perception

Computer Vision, a subset of Deep Learning and AI, is essential for how autonomous vehicles observe their surroundings both on and off the road. They must rely on a multitude of different sensors and cameras to collect data about road conditions. LIDAR (light detection and ranging) is especially helpful, emitting beams of light outward to create a 3D model for the AI to observe similar to a bat’s echolocation abilities. 

Autonomous vehicles rely on a series of more traditional sensor technology as well like radar and cameras. These sensors can be used to collect data on everything from the weather and geography to make navigation more efficient. Cameras are included too and provide the most obvious assistance to a vehicle’s vision. 

Control Algorithms and Precision Driving

Controlling and operating an autonomous vehicle is a complex process for AI models because they have to process all of the data they’ve perceived and use to guide a vehicle through public roads where mistakes are not an option. To do this, Deep Learning models use feedback loops that help AI models respond to data inputs from sensors around the car, helping the vehicle make necessary adjustments. 

Ride comfort must also be taken into account as these vehicles will most likely be transporting people as well as goods. So, it’s necessary that autonomous vehicles don’t operate without taking passenger comfort into account by avoiding high-speed turns and other maneuvers that might displace passengers. 

One of the ways that AI models are adapting to increase passenger comfort is by updating their driving models to be proactive over-reactive. Traditionally, autonomous AI models were made to react to conditions observed immediately on the road, but, with more advanced algorithms being developed, it is now possible to create AI models that can predict future conditions based on observing the cars driving in front of them. 

Connectivity and V2X Communication

Swarm intelligence and internet-of-things (IoT) also play crucial roles in the development of autonomous vehicles and vehicle-to-vehicle communications. This allows autonomous vehicles to share information to help navigate the roads together, making traffic safer and less congested. 

Over time, these abilities can extend further into traffic infrastructure such as street lights and stop signs that help autonomous vehicles predict upcoming road conditions that they must follow. Applications can also be extended towards parking lots and garages where parking information can be analyzed to increase parking availability in busy cities. 

Challenges and Ethical Considerations

Despite their advancements over the past decade, pedestrians and other drivers are still cautious about the wide-scale implementation of autonomous vehicles on public roads because they still do not believe that they are as safe as human drivers. This problem can be exponential too, with autonomous vehicles not only making poor driving decisions, but also losing control or being affected by a bug that might cause it to drive wildly against human driver engagement. 

Cybersecurity also poses a major threat as a network of autonomous vehicles connected by swarm intelligence creates an interesting target for cybercriminals. By taking command of a small fleet of autonomous vehicles in a city, criminals could wield a disturbing amount of power over a community, so it’s necessary for robust security features to also be developed for autonomous vehicles. 

Future Prospects

Self-driving cars have often been associated with the future and we’ve never been closer to seeing their wide-scale adoption across the world. Companies like Tesla and Alphabet are reshaping how we use vehicles for transportation with their self-driving AI models and intricate mapping.

As new self-driving cars continue to be produced, it won't be long until we see a systemic shift in our road infrastructure, with AI taking over more and more of our driving duties. While this may scare some people with the fear that AI is dangerous, there is no doubt that it’ll remove countless drivers from the road that were already dangerous.