The adoption of autonomous vehicles on a worldwide scale is picking up speed. The UK has recently passed the Automated Vehicles Act in an effort to establish the secure integration of fully and partially autonomous vehicles into society over the subsequent few years. More autonomous vehicles are being tested immediately in China than anywhere else on the planet. And in the USA, major metropolitan areas have enlisted using “robotaxis” of their public transportation capabilities. Firms like Cruise, Waymo and, in fact, Tesla all have billions of dollars invested of their grand ambitions of hosting self-driving cars and services from coast-to-coast and everywhere in the world. At this point, the event and implementation of autonomous vehicle technology isn’t any longer a matter of ‘if’ or really even ‘when’, but simply a matter of ‘to what extent’? What can the adoption of AI-powered autonomous vehicles on a large scale do to enhance not only our roadways but our society?
Automating Road Safety
The push for autonomous vehicles and the mountains of capital invested in these technologies is indicative of the widely acknowledged public good that the deployment of self-driving cars can have. For starters, there are the protection standards of self-driving vehicles over that of human drivers. A recent study published in Nature Communications and insights from Tesla’s 2022 Impact Report underscore the transformative potential of autonomous vehicle implementation on the enhancement of road safety. AVs have been found to cut back rear-end, head-on and lateral collisions, in addition to incidents of running off the road, by 20% to 50%. Provided that the World Health Organization estimates that road traffic injuries are answerable for the death of 1.35 million people annually internationally, this dramatic improvement in automobile safety would have a seismic impact. While more technological refinement continues to be required before autonomous vehicles outperform human drivers in all circumstances (human-driven cars still remain safer in low-visibility conditions and through turns), the further advancement of sensor technologies, predictive algorithms and V2X communications will proceed to enhance responses in these complex driving scenarios and enable them to create safer roadways.
AV-oiding Traffic
As well as, the deployment of autonomous vehicles may also have a major impact on the problem of traffic congestion. In a study conducted by the Association for Commuter Transportation (ACT) and the USA Department of Transportation (USDOT), “rush hour” commutes—once an appropriate title—now make up six hours a day and make travel during these heightened times take 40% longer. A single individual braking can impact traffic across town, triggering a slowdown and even complete gridlock. With the assistance of sensors and cameras powered by cutting-edge software, nevertheless, autonomous vehicles brake far less often than their human counterparts and, because of this, are far less more likely to cause these traffic disturbances. Even deploying a couple of autonomous vehicles can have a positive effect on traffic congestion by helping to moderate the speed of the human drivers they share the road with.
Fuel Efficiency and Sustainability
Autonomous vehicles may also improve fuel efficiency over human drivers by controlling their speed and acceleration and by traveling closer together in order to enhance air drag and reduce fuel consumption. In accordance with MIT News, if every vehicle on the road were autonomous, not only would travel speeds be boosted by 20%, but we might see fuel consumption reduced by 18% and carbon dioxide emissions lowered by 25%. This development could be pivotal in our continued efforts to bring sustainability to untold numbers of industries and businesses. A study by TuSimple found that their autonomous trucks were 11% more fuel efficient than those piloted by human drivers. This increased fuel economy will allow goods and services to turn out to be more cost effective to consumers while also aiding these firms of their efforts to make their operations greener and more sustainable.
A Look Under The Hood
The extent of technological advancement that enables these autonomous vehicle systems to operate have been many years within the making. Arrays of sensors, including cameras, radars, and LiDARs feed data into neural networks designed to mimic the human brain and perform object detection and image segmentation. These neural networks then process this sensory input, including the presence of other vehicles, road signs and obstacles, in an effort to create a comprehensive surround map of the vehicle’s environment. The subsequent step is then motion planning, where detailed routes and trajectories are calculated using a comprehensive evaluation of all of the previously collected data. Even then, these processes all still have to account for unseen situations and have the opportunity to adapt in real-time to those circumstances. Resulting from the large variety of intricate and detailed processes that go into the event of those systems and software, no two are alike and every of those AV systems has their pros and cons.
Forks In The Road
The 2 primary approaches to the event of autonomous driving are HD maps versus HD map-less systems. The advantage of using maps is in its simplified object detection and motion planning, but these systems are depending on continuous communication for data updates and are susceptible to obsolescence. HD map-less systems, just like the one developed by autonomous driving software company Imagry, rely almost entirely on real-time data and are more in step with how human drivers operate. Additionally they are more self-sufficient and fewer vulnerable to cyber threats, but do require advanced onboard perception capabilities and complicated real-time processing. After this initial split in philosophy, there exists several others which have been on the forefront of some debate within the industry. Rule-based vs. Neural Network-based motion planning is one such sticking point with safety and regulatory bodies preferring the more definable “if-then” approach that’s the hallmark of rule-based systems. While the development of predefined scenarios offers high explainability, these systems struggle to adapt to recent, unexpected situations, an area where neural network-based systems excel.
The Road Ahead
The groundwork continues to be laid to enable the widespread adoption of autonomous vehicles everywhere in the world. There may be definitely no shortage of automakers and corporations willing to take a position billions of dollars into the event of autonomous vehicles and services centered around them. While there remain many various systems and processes that go into the creation of self-driving vehicles, all experts in some capability agree on the vast amount of practical advantages that autonomous vehicles and their implementation can have for society. The subsequent and maybe most significant hurdle to clear is increase most people’s trust in these technologies. The advancement of artificial intelligence also began beneath a cloud of skepticism and mistrust that needed to be overcome. Now, there shouldn’t be a serious industry or company on the planet that doesn’t utilize these technologies in some capability or one other. Autonomous vehicles may have an identical hill to climb, but as these systems advance and turn out to be more prevalent on our roads, our comfortability and familiarity with them may also only increase. As these technologies advance at a rapid pace, the AV industry is farther down the road to global adoption than some might think.
