The current progress of autonomous driving technology is knocking at our agency. Many people still regard self-driving cars as the stuff of science fiction, but these cars are closer than you might think: there are already prototypes being tested on public roads and semi-autonomous systems have been introduced in a wide variety of vehicles on the market today. We should expect the widespread use of autonomous driving technology to transform industries, enhance road safety, reduce traffic congestion and revolutionise urban planning.
Autonomous driving technology is rapidly evolving and seeks industry dominance, but what are the state of the art advancements powering it, and what are the challenges that remain? Safety concerns are primary, as we’re used to having direct control over our vehicles. The policy industry has mirrored this, with 8,820 agencies working on rules for autonomous vehicles in the US alone. In this article, we cover the state of the art autonomous driving technology, the main features that are developing its future, the barriers that remain to implementation, and the benefits of a fully autonomous future.
Levels of Autonomous Driving
In order to comprehend the future of autonomous driving, it is very important to understand the six levels of driving automation defined by the Society of Automotive Engineers (SAE): level 0 (no automation), level 1 (driver assistance), level 2 (partial automation), level 3 (conditional automation), level 4 (high automation, the ability to drive in most circumstances), and level 5 (full automation, without the need for a driver).
Level 0 – No Automation: All aspects of the dynamic driving task are performed by the human driver. In part II of the report, ‘Specifications for a Level 4 Automated Vehicle’, SAE expands on the complexity of potential features and interactions between them. At Level Four, the driver has little, if any, control at certain times and no control at others. Level Four can be best understood by using the metaphor of airplane autopilot – the vehicle operates fully autonomously, though the human driver retains the ability to override it.
Level 1 – Driver Assistance: The vehicle can assist the driver with either steering or acceleration/deceleration, but the driver is still completely in control of the car.
Level 2 – Partial Automation: The vehicle can control both steering and acceleration/deceleration, but the driver must remain engaged at all times and always be prepared to retake control.
Level 3 – Conditional Automation: The vehicle is able to execute most driving tasks but will request the driver to intervene under certain circumstances.
Level 4 of the spectrum – High Automation: The vehicle can drive itself within specific conditions (for example, in designated urban areas), and driver intervention is not required under these conditions.
Level 5 – Full Automation: The vehicle can perform all driving functions under all foreseeable circumstances. Note that for the purposes of this classification, ‘vehicle’ refers to conventional road vehicles as well as other structures (for example, RoboBees) used on traditional roadways.
Most of the cars sent out on to the roads today lie somewhere on the spectrum between Levels 1 and 2, like Tesla’s Autopilot and GM’s Super Cruise. But the technological path forward in autonomous driving is one that ultimately involves reaching Level 5, where the cars can drive themselves under all conditions – without human oversight.
Key Technologies Driving Autonomous Vehicles
Autonomous driving technology mainly involves multiple layers of hardware/software systems that serve as the eyes, brain and steering wheel of autonomous vehicles. Here are some major technologies of autonomous driving:
1. Sensors and Cameras
With an array of sensors, autonomous vehicles can literally ‘see’ the world around them and assign meaning to it: cameras, velocity-detectors, ring-laser gyroscope sensors, GPS, corner-censors, Pyroelectric infrared sensors, heck, even flash guns.
Light Detection and Ranging (LiDAR): Using a laser to measure the distance or depth of objects in its environment, liDAR creates a 3D map of the vehicle’s surroundings. This information helps the vehicle to steer in the proper direction while also detecting other objects relative to its position (eg, 30ft from the right front bumper).
Radar: The operation of radar sensors is based on radio waves that allow detecting the speed and location of other vehicles, pedestrians and other obstacles, irrespective of adverse weather.
Cameras: The video generated by cameras allows the vehicle to see and ‘read’ the infrastructure ahead, including road signs, traffic lights and lane marking.
Ultrasonic sensors: They are used for short-range detection (parking aid, …) [it detected objects at a safe distance from the vehicle].
These sensors are combined to create an evolving real-time model of the vehicle’s surroundings. Crucially, autonomous vehicles like a self-driving car don’t stop responding to new data; they continue to test and refine the model against incoming data to identify any anomalies.
2. Artificial Intelligence (AI) and Machine Learning
Artificial intelligence (AI) and machine learning underlie autonomous driving systems because these decision-making tools are able to compute information from sensor data.
AI Decision-Making: The AI algorithms sense data from various sources such as video cameras, thermal sensors and light detection instruments to make the car navigate, avoid obstacles and react to the changing traffic conditions. For example, AI can enable the vehicle to identify a pedestrian crossing the street or the most optimal route to avoid congested traffic.
Machine learning: autonomous cars adapt to new circumstances as they learn from experience, gradually improving their performance over time Think about it this way: a self-driving car’s control system generates a data set containing feedback about how well it is doing within different driving scenarios The sensors enable the car to read feedback from the environment, and machine learning analyses the patterns and outcomes in order to devise a more adaptive control strategy for handling new and unforeseen circumstances.
3. High-Definition Maps
While GPS (or similar systems) provides base navigational data, self-driving cars require much more detailed maps that show precise features of the road. These high-definition maps reveal everything from how roads are laid out, to where lane markers are painted, including speed limits, stoplights, and other signals controlling the flow of traffic. Furthermore, the maps need to be constantly updated with information about new construction zones, new traffic controls, special-event street closures and other street and traffic changes.
These maps are used by autonomous vehicles to plan routes and predict upcoming turns, in combination with real-time sensor data.
4. Vehicle-to-Everything (V2X) Communication
V2X communication connects autonomous vehicles – such as cars, trucks and buses – to other vehicles as well as infrastructure (including traffic lights) and people (as pedestrians or cyclists). V2X communication enables both vehicles and human users to share information in real time in order to safeguard and improve efficiency in autonomous driving.
Vehicle-to-Vehicle (V2V): Vehicles can exchange information such as speed, location and direction, so you could reduce the chances of collision or even participate in co-operative driving, where vehicles need to coordinate several manoeuvres, for instance when mer
Vehicle-to-Infrastructure (V2I): V2I communication links vehicles to traffic lights, signs, and other road infrastructure, impoving traffic management, vehicle flow, and reducing congestion.
Vehicle-to-Pedestrian (V2P): V2P systems communicate between vehicles and pedestrians and cyclists, increasing their effective presence to vehicles, particularly in urban environments.
This indicates a deeper integration of V2X technology into the development of driverless cars. Soon, V2X technology will yield large gains in safer and more efficient autonomous driving.
Benefits of Autonomous Driving Technology
Here are three of the biggest advantages of autonomous driving technology once it is fully developed and available on the roads. 1. Traffic casualties could be virtually eliminated. 317,361 people lost their lives on the roads in 2013. With driverless vehicles, these number could be halved. 2. Vehicles would use the roads more efficiently. Traffic jams are a fact of life. Autonomous vehicles could spread out more evenly on the roads. Through intelligent coordination, traffic congestion would be significantly reduced. 3. Commuting would be easier and more convenient. Imagine dropping off the kids at school, starting the journey and then relaxing in the back of the car with a cup of coffee. Cars would pick up you up when you’re ready. Or, if you wanted to become more productive, you could use this time to do business, study, read or simply sleep on the way to work.
1. Improved Road Safety
Topping this list is eliminating human error, which is estimated to cause as many as 94 per cent of traffic accidents. Autonomous vehicles won’t fall asleep at the wheel, get distracted from smartphones or be drunk driving. They also can respond more quickly than humans to fast-moving road hazards. AI-powered vehicles might be able to eliminate 90 per cent or more of accidents and fatalities.
2. Increased Efficiency and Reduced Traffic Congestion
By talking to each other, sensing real-time traffic conditions around them, and adjusting how they drive, on-the-fly, the vehicles can even work to keep traffic flowing more efficiently. Through improvements in after-start acceleration and braking, not to mention Lane merging that removes the need for braking in stop-and-go traffic, AVs can markedly reduce traffic jam.
One prospective efficiency gain relates to a driving technique known as platooning, where autonomous vehicles follow close to each other at high speeds. This allows each car to take advantage of the turbulent air (or ‘draft’) generated by the car directly in front, reducing aerodynamic drag for every car in the group. Made possible by the intraregional nature of cars’ radar sensors, such caravans could, in turn, cut fuel consumption and thus emissions.
3. Enhanced Mobility for All
Removing humans from the driving equation would expand the safety benefit that autonomous driving technology can provide to existing populations such as the elderly, the disabled and people in economically disadvantaged parts of the world who do not have access to regular public transportation. For these individuals, self-driving cars could improve their quality of life by providing independence and mobility.
Furthermore, ride-shaping services might be more attractive and affordable with autonomous fleets, given that these companies Uber and Lyft are gearing up to bring autonomous vehicles into their services.
4. Optimized Urban Planning
As a result of an increasing presence of autonomous vehicles in the transportation network, cities could be redesigned to accommodate to these new transportation models: a shift towards transit-related travel can reduce vehicular ownership, leading to a decrease in personal parking spaces, wider roads and a shift from automobile dependence towards safer and efficient facilities and infrastructure favouring city dwellers.
Autonomous vehicles could also be readily integrated with public transport networks to offer first- and last-mile feeder services to passengers, leading to more efficient, multimodal networks and diminished dependence on the private car.
Challenges Facing Autonomous Driving Technology
In spite of the fast pace of innovation in autonomous driving, some key issues will have to be resolved before automated-driving companies and car manufactures can roll out fully autonomous vehicles on a large scale.
1. Regulatory and Legal Hurdles
Again, the regulatory terrain is in constant flux since many governments, in particular, will struggle to catch up with technological progress. The biggest issues concern liability in accidents with autonomous vehicles, which remains a pressing concern. There are also questions of safety standards, and, most obviously, the protection of private data.
2. Safety and Ethical Concerns
While AVs might be safer than humans for most scenarios, logic compels us to consider how these systems will respond ethically in a situation where, for instance, a collision is inevitable; should it kill its occupants or the pedestrians? Such ethical dilemmas implicate a range of decision-makers.
Additionally, autonomous systems must be kept safe from hacking, with operative safeguards, to ensure that such vehicles don’t go haywire in the event of a cyberattack or system malfunction – a scenario that could have catastrophic effects if a vehicle is on the road.
3. Public Trust and Acceptance
Autonomous vehicles will have to prove in the marketplace that the public will trust them. Some people are rightfully intrigued by the wildly promising potential of self-driving vehicles, yet others recoil at the notion of letting go of the wheel to a machine. By submitting today’s pilot programmes to robust testing, public demonstrations of safety, and clear communication regarding what the technology can and cannot do for us, we’ll ensure that AVs possessed of wide social acceptance can thrive.
4. Infrastructure Development
Widespread driverless vehicle deployment, particularly later levels of autonomy (such as Levels 3-5), will require new infrastructure, including smart roads, new traffic-management systems, and high-speed communications networks for V2X technology. This includes large investments from governments and industry sectors on a coordinated scale, and proper planning of city infrastructures to support them.
The Road Ahead: Fully Autonomous Future
If there’s one thing to be hopeful about despite all these problems, it’s the progressive advance of autonomous driving technology. Some of the largest worldwide car manufacturers – including Tesla, Waymo and General Motors – and the well-known tech companies such as Google and Apple are developing increasingly automated systems in the hope they can produced fully autonomous vehicles. As the technologies mature further, we can safely assume more autonomous features will permeate into consumer cars, and that Level 4 and Level 5 autonomy will become more of a reality.