How AI makes autonomous vehicles safer and more sustainable?
Updated: Mar 15
Autonomous vehicles have been a hot topic for several years now, and with the rapid advancements in Artificial Intelligence (AI), they are becoming a reality faster than ever. While the concept of self-driving cars may seem like something out of a sci-fi movie, the potential benefits of these vehicles are undeniable. One of the biggest advantages of autonomous vehicles is their ability to make transportation safer and more sustainable. In this blog post, we will explore how AI makes autonomous vehicles safer and more sustainable.
Can it be safer than human driving?
The answer is yes, autonomous vehicles have the potential to be safer than human driving. According to a report from the National Highway Traffic Safety Administration (NHTSA), 94% of all car crashes are caused by human error. Autonomous vehicles have the potential to greatly reduce this number, as they are not susceptible to many of the errors and distractions that cause accidents.
While human drivers have the ability to make decisions based on experience and intuition, they are also prone to errors and distractions, which can lead to accidents. Autonomous vehicles, on the other hand, are equipped with sensors, cameras, and advanced algorithms that allow them to make split-second decisions based on real-time data, without the risk of human error like distraction, fatigue, or emotional reactions. These sensors can detect obstacles, pedestrians, and other vehicles on the road, allowing the car to make quick decisions and adjust its speed and direction accordingly. In addition, the AI-powered algorithms running in the car's system can analyze the data collected from these sensors and make informed decisions in real-time. With autonomous vehicles, the chances of accidents due to human error are greatly reduced, which can lead to a significant decrease in traffic fatalities.
Black-box algorithms really make this happen?
Black-box algorithms are an integral part of autonomous vehicles, helping them to navigate the road and make decisions in real-time. These algorithms work by taking input from sensors and cameras mounted on the car, processing the data, and generating an output that determines how the car should respond. Here's a more detailed breakdown of how black-box algorithms work in autonomous vehicles:
The input data for black-box algorithms in autonomous vehicles comes from various sensors, cameras, and other sources, including lidar, radar, GPS, and optical sensors. These sensors collect data on the car's surroundings, including other vehicles, pedestrians, road signs, and traffic lights. The data is then fed into the black-box algorithm, which processes it to generate an output.
Noticeably, to make this data usable for the algorithm in the next steps, they need to be accurately collected and annotated. Pixta AI is the leading company offering the service of data sourcing and data annotation with low cost, high quality and full of compliance to train all kinds of your models. Don't hesitate to contact us or leave your email to receive free consultation and get a 20% discount for all off your demand right today!
Once the input data is collected, the black-box algorithm processes it using a variety of techniques, including machine learning, deep learning, and computer vision. These techniques allow the algorithm to identify objects, recognize patterns, and make predictions about what might happen next on the road. The processing can also include data from other sources, such as traffic data or weather reports, to help the algorithm make more informed decisions.
Although AI is often equated with 'deep learning' neural networks, the artificial intelligence ecosystem encompasses many types of algorithms.
Image: Narrative Science
The output generated by the black-box algorithm is the car's response to the input data. This can include steering the car, accelerating or braking, changing lanes, or adjusting the car's speed. The output is generated in real-time and is continuously updated based on the data received by the sensors.
Black-box algorithms in autonomous vehicles also receive feedback from the car's actions. For example, if the car changes lanes, the algorithm receives feedback on whether the maneuver was successful or not. This feedback is used to adjust the algorithm and improve its performance over time.
More sustainable for autonomous vehicles
According to the World Health Organization, air pollution from cars causes an estimated 7 million premature deaths worldwide each year. With autonomous vehicles, the potential for electric and hybrid cars becomes more feasible, as they can be programmed to take the most efficient routes and avoid idling. This not only reduces emissions but also saves fuel and reduces congestion.
In addition, AI algorithms can help manage energy consumption in autonomous vehicles by controlling various systems such as heating and air conditioning, lighting, and battery usage. By optimizing these systems, the energy consumption of the vehicle can be reduced, resulting in less pollution. AI can also work in smart charging too, it can optimize charging times and locations for electric autonomous vehicles, taking into account electricity prices, grid stability, and renewable energy availability.
In conclusion, the integration of AI in autonomous vehicles is transforming the way we think about transportation. By enabling autonomous vehicles to make data-driven decisions in real-time, AI is making these vehicles safer and more sustainable than ever before. Through route optimization, energy efficiency, predictive maintenance, eco-driving, and smart charging, AI is reducing the carbon footprint of autonomous vehicles while also improving their safety on the road. As AI technology continues to evolve, we can expect to see even more innovations that will revolutionize the transportation industry, making our roads safer, and our planet more green.