Week 2 - Post 1: MBA 6101 - What Impact Does AI Provide on Self Driving Cars?

 AI and Self Driving Cars

When it comes to the vehicles on the road today, many people do not realize that a lot of them might be using Artificial Intelligence to provide self driving capabilities. AI and self driving vehicles have received a lot of praise while also receiving a lot of backlash and pushback on their safety and necessities in our society. While some push that these artificial features will push the limits on safety, others claim safety is neglected and people will use it as an excuse to not have to actually drive. Across all social media platforms, it has been seen numerous times, videos of people sleeping behind the wheel, texting, putting on makeup, or even reading books while the car does all the work. If this is what society is turning to, the question left on the table is, what all goes into self driving vehicles, and how is AI driving these features? 

AI plays a significant role in the development and operation of self-driving cars. Self-driving cars, also known as autonomous vehicles, use various AI technologies to perceive and interpret their surroundings, make decisions, and control the vehicle's movements. the following are a handful of the pieces that go into self driving (autonomous) vehicles and the importance they have on the safety of both the driver and others around. 

Perception: Self-driving cars rely on sensors such as cameras, lidar, radar, and ultrasonic sensors to gather information about their environment. AI algorithms process the sensor data to recognize and classify objects, such as pedestrians, vehicles, and traffic signs.

Mapping and Localization: AI algorithms are used to create detailed maps of the vehicle's surroundings and determine the car's precise location within that map. This helps the car understand its position and navigate accurately.

Decision Making: Self-driving cars use AI algorithms to analyze the sensor data, map information, and other relevant data to make decisions in real-time. These decisions include choosing the appropriate speed, changing lanes, braking, and reacting to unexpected situations on the road.

Control Systems: AI algorithms are responsible for controlling the vehicle's movements, including acceleration, steering, and braking. These algorithms continuously process sensor data and adjust the vehicle's actions to ensure safe and efficient driving.

Machine Learning: Machine learning techniques, such as deep learning, are crucial in self-driving cars. By training AI models on large datasets, self-driving systems can learn from past experiences and improve their performance over time. Machine learning enables the car to recognize patterns, anticipate behavior, and make predictions.

Safety and Redundancy: Self-driving cars incorporate AI-based safety measures to ensure the well-being of passengers and other road users. Redundant systems and fail-safe mechanisms are implemented to handle failures or unexpected events. AI algorithms help detect anomalies, monitor the performance of critical components, and take appropriate actions to mitigate risks.

While AI and self-driving vehicles might be the way of the future, It's important to note that while significant progress has been made in self-driving car technology, there are still challenges to overcome before they become commonplace on our roads. Safety, reliability, regulatory frameworks, public acceptance, and infrastructure are among the key areas that require further attention and development and with the advancements we see in AI, these may come sooner rather than later.