In the tech field, artificial intelligence cars and self-driving automobiles are frequently seen as complementary issues. Simply put, you can't talk about one without talking about the other.
Though AI is being rapidly applied in multiple sectors, the way it is being implemented in Automotive remains a contentious subject. In addition to every auto manufacturer and their mother racing to create artificial intelligence and self-driving technologies, there is a swarm of software companies and startups pursuing the same goal.
Types of Automotive AI
AI for Autonomous Vehicles
Many major automakers and SMEs are developing their own autonomous vehicles and driving technologies. Among the examples of this work, we could name the following:
- The robotaxi pilot and an operational commercial robotaxi service. It prioritizes safety at all times by utilizing three sensor types — LiDAR, radar, and cameras. It has already demonstrated a great outcome in self-driven rides with almost no incidents.
- The development of driverless vehicles capable of safely transporting people from point A to point B. It utilizes 360-degree perception technology capable of detecting pedestrians, other vehicles, cyclists, road work, and other obstacles from up to 300 yards away.
- Retail-based self-driving vehicles, developing fleets for robotaxi and driverless grocery delivery services. The mix of AI algorithms, sensors, real-time cameras, and thousands of virtual and real-world test kilometers ensures safe driving decisions.
AI for Auto Manufacturing
With over 80.4 million automobiles expected to be produced globally by 2022, it's no surprise that manufacturers are looking for machinery and techniques to improve production.
Artificial intelligence in the automobile sector is changing not only the cars on the road, but also the factories that make them and their maintenance operations.
Here are a few examples of how smart machinery and AI-powered systems are increasing the efficiency of automobile production lines.
- Linking automakers to a data pipeline that uncovers valuable insights from insurance companies and service centers, allowing access to information on how to make vehicles safer and more lasting.
- Robots are being manufactured to automate workplace operations that are too time-consuming, messy, or even dangerous for human workers. Though robots have been used on the automotive manufacturing line for many years, they have never worked alongside humans. These robots assist humans in the supply chain by tending to machinery, handling supplies, completing tests, and packing finished goods.
AI for Driver Assistance
If you have a new car that can parallel park itself, you have a vehicle that is equipped with an AI-powered advanced driver-assistance system.
Automatic braking, driver drowsiness detection, and lane departure warning are just a few examples of ADAS and car HMI.
Some technologies go beyond what is already available in many major vehicle models, with businesses employing them to re-train commercial drivers and avoid collisions within their fleets.
Here are some examples of how car artificial intelligence is being used to improve road safety through driver-assist systems.
- White-label software that adds conversational voice and intelligence capabilities to technology products in industries such as Automotive, Communication, Health, and Aviation. Natural conversational AI is used in vehicles to improve the comprehension of context, emotion, complex sentences, and user preferences by utilizing speech recognition, natural language understanding, speech synthesis, and smart avatars.
- An ADAS for personal vehicles, fleets, ride-sharing services, or auto insurance firms. Driving analysis and real-time notifications could be provided in a car with artificial intelligence to warn drivers of potential hazards such as lane departure, forward collisions, and dangerous driving conditions.
- Artificial intelligence sensor technology for commercial fleets. By analyzing driver behavior, an intelligent driver system lowers distracted driving, which contributes to collisions. The data is used by the system to keep drivers alert enough to prevent crashes and traffic infractions. With video and facial recognition, it can even help businesses process insurance claims more effectively.
AI for Autonomous Delivery
Takeout and supermarket delivery services are becoming increasingly popular, as customers demand their purchases to come faster and more effectively. By 2025, the internet meal delivery services business is expected to be worth more than $192 billion.
Artificial intelligence is infiltrating the field, allowing self-driving vehicles to fulfill delivery orders. Here are some examples of AI-powered autonomous delivery services in the automotive industry.
- A self-driving robotic delivery solution for last-mile goods from restaurants, pharmacies, and grocery stores that enables faster, cheaper, and safer delivery that meets customer expectations.
- Self-driving delivery robots that travel at pedestrian speeds while navigating obstacles and delivering packages, groceries, and meals to customers who have placed orders.
- A robotic last-mile delivery network of over 500 AI-powered, all-electric robots. Users can use an app to place restaurant orders and then monitor their autonomous delivery in real time.
What are self-driving cars and how they work
A self-driving car (also known as an autonomous car or driverless car) is a vehicle that travels between destinations without the assistance of a human operator by utilizing sensors, cameras, radars, and artificial intelligence. To be considered fully autonomous, a vehicle must be able to go to a predetermined location without human involvement on roads that have not been modified for its usage.
Audi, BMW, Ford, Google, General Motors, Tesla, Volkswagen, and Volvo are among the companies developing and/or testing self-driving automobiles.
How self-driving cars work
AI technologies are at the heart of self-driving automobile systems. Self-driving car developers use massive volumes of data from image recognition systems, as well as machine learning and neural networks, to design systems that can drive independently.
The neural networks recognise patterns in the data, which are then fed into machine learning algorithms. Images from cameras on self-driving cars are among the data sources from which the neural network learns to recognise traffic signals, trees, curbs, pedestrians, street signs, and other elements of any particular driving environment.
Cars with self-driving features
Google's Waymo project is an example of a nearly fully autonomous self-driving car. A human driver is still required, but only to overrule the system when necessary. It is not truly self-driving, but it can drive itself in perfect conditions. It has a great degree of independence. Many of the automobiles on the market now have a lower level of autonomy but still have some self-driving technologies. As of 2019, the following self-driving features are available in numerous production cars:
- Hands-free steering centers the vehicle on the road without needing the driver's hands on the wheel. The driver must continue to pay attention.
- Adaptive cruise control (ACC) maintains a preset distance between the driver's vehicle and the vehicle in front.
- When the driver crosses lane markings, lane-centering steering automatically nudges the vehicle toward the opposite lane marking.
Levels of autonomy in self-driving cars
The National Highway Traffic Safety Administration (NHTSA) of the United States defines six levels of automation, beginning with Level 0, where all the driving is done by people. The following levels progress through driver assistance technology towards completely autonomous vehicles. The five stages that follow Level 0 automation are as follows:
Level 1: An advanced driving assistance system (ADAS) assists the human driver with steering, braking, and acceleration, but not all at the same time. An ADAS system incorporates rearview cameras as well as features such as a vibrating seat warning to inform drivers when they leave their travel lane.
Level 2: An ADAS that can steer, brake, or accelerate while the driver remains fully conscious behind the wheel and still acts as the main driver.
Level 3: Under specific conditions, such as parking, an autonomous driving system (ADS) can conduct all driving responsibilities. In these cases, the human driver must be prepared to reclaim control and must remain the primary driver of the vehicle.
Level 4: Under specific conditions, an ADS can conduct all driving tasks and monitor the driving environment. In some cases, the ADS is dependable enough that the human driver does not need to pay attention.
Level 5: The vehicle's ADS acts as a virtual driver, driving the vehicle in all conditions. The human occupants are only expected to be passengers and are never meant to drive the vehicle.
The pros and cons of self-driving cars
The primary advantage highlighted by proponents of self-driving cars is safety. According to a DOT and NHTSA statistical prediction of road fatalities, 42,915 people died in motor vehicle traffic incidents in 2021. According to the NHTSA, 94% of serious crashes are caused by human error or poor decisions, such as drunk or distracted driving. Autonomous vehicles eliminate those from the driving process, though they are still subject to other factors that cause crashes, such as mechanical faults.
The economic benefits of driverless automobiles could be tremendous if they can drastically reduce the number of crashes. According to the NHTSA report, injuries have an economic impact of $57.6 billion in missed business productivity and $594 billion in loss of life and impaired quality of life.
Theoretically, if autonomous cars dominated the roads, traffic would flow more smoothly and there would be less congestion. The occupants of fully automated cars could perform constructive tasks while going to work. People who are unable to drive owing to physical constraints may gain new independence from autonomous vehicles and be able to work in industries that require driving.
Autonomous trucks have been tested in the United States and Europe to allow drivers to use autopilot over long distances, allowing them to rest or complete tasks while also improving driver safety and fuel efficiency. If you are driving a non CDL or a CDL truck, you should know can i get my cdl back after 10 years?
One disadvantage of self-driving technology is that traveling in a vehicle without a person behind the wheel may be unsettling at first. However, as self-driving capabilities grow more popular, human drivers may become overly reliant on autopilot technology, leaving their safety in the hands of automation, even when they should be acting as backup drivers in the event of software faults or mechanical issues.
Self-driving car safety and challenges
Autonomous vehicles must learn to recognise a wide range of items in their route, from branches and garbage to animals and people. Tunnels that interfere with the Global Positioning System (GPS), building projects that necessitate lane changes, and difficult judgments, such as where to stop to allow emergency vehicles to pass, are all obstacles on the road.
The systems must make rapid decisions on when to slow down, swerve, or maintain regular acceleration. This is an ongoing difficulty for developers, and there have been reports of self-driving cars pausing and swerving excessively when objects on or near highways are identified.
With crashes comes the issue of liability, and policymakers have yet to establish who is responsible when an autonomous vehicle is involved in an accident. There are also substantial fears that the software used to drive self-driving vehicles could be hacked, and automakers are working to solve cybersecurity problems.
Carmakers must adhere to Federal Motor Vehicle Safety Criteria (FMVSS), and the NHTSA states that more effort is needed to ensure that automobiles fulfill those standards.
Autonomous Vehicle Benefits
Some argue that the greatest danger of autonomous cars is that people believe they comprehend it too soon. In reality, most people do not. For the record, AI and cars have a mutual history, and it was scientists' dream to create intelligent machines that could think and act for themselves that gave birth to autonomous vehicles, or self-driving cars, which have proven to be one of the best AI technology innovations. Let's look at some of the advantages of these cars.
Neural networks and particular algorithms are used in autonomous vehicles. As was already mentioned, these are object detection techniques based on AI and Machine Language. These are used to collect data, assess objects, and make sound decisions while driving. These characteristics also allow these intelligent robots to propose answers to problems in advance by forecasting events through rapid data processing.
For example, autonomous vehicles can detect a potential threat, such as a car collision ahead or behind them, and make a real-time choice to avoid it. These pieces of information are processed using good data gathering sensors, and the results are converted into actions. Self-driving cars feature five basic components that allow them to improve operation in real time, in addition to the neural network and particular algorithms: computer vision, sensor fusion, localization, path planning, and control. They also have improved AI perception technology for recognising pedestrians, vehicles, bicycles, work, and obstructions up to 300 metres away. These inbuilt algorithms assist these vehicles in determining and recommending alternative routes based on real-time traffic circumstances. It's a very impressive technology.
Tesla recently produced self-driving electric cars fitted with autopilots to enable automatic steering, accelerating and braking, lane change, and parking activities. In addition to these characteristics, these vehicles have the ability to cut global emissions, which is a significant achievement for fuel-powered vehicles. Today, autonomous vehicles can be found in some of the world's largest cities. Heavy-duty trucks without drivers that can transport goods over long distances have also been produced. This has not only lowered transportation costs but also the loss of human lives due to accidents, the majority of which are caused by human error.
Some automakers have recently produced autonomous vehicles with enhanced AI features such as personal AI assistants, radar detectors, and cameras, all of which serve to prioritize security among other functions. These self-driving cars have AI-enhanced features that are a significant improvement over their predecessors. Self-driving cars can learn about the driver's habits, such as driving speed, desired car temperature, driving mood, observance of traffic signs, favorite songs, or radio stations. These autonomous vehicles have also assisted in changing bad driving habits and behaviors by rating the vehicle owners' driving skills.
Autonomous Vehicle Drawbacks
Despite being one of the most anticipated technologies of the twenty-first century, AI and autonomous vehicles have been linked to a variety of issues:
- Autonomous vehicles are limited to more restricted environments and clearer weather. Sensors, like human eyes, do not perform well in fog, rain, or snow.
- Maps and sensors are essential for autonomous vehicles to perform properly. Unfortunately, these maps have limited test areas at the present. Creating and maintaining maps for self-driving cars is a complicated and time-consuming challenge that has yet to be overcome. The testing areas will also need to be expanded. In the U.S. for instance, precise maps would have to be produced and maintained across the 4 million miles of public road and this is no minor work.
- Tesla's self-driving vehicles have prompted some safety worries. They were observed violating double yellow lines and driving into oncoming traffic, failing to stop for cars crossing the street, and driving into metal poles and roadside stones.
- AI-controlled autonomous vehicles are unable to participate in complicated social interactions with other drivers, bikers, and pedestrians. To navigate these situations, generalized intelligence and common sense are required, which robots currently lack.
- Tesla's autopilot has trouble detecting flashing lights, temporary road and traffic maintenance cones, and most emergency vehicles traveling in the opposite direction. Again, the majority of the crashes occur in the dark, indicating a flaw in the autopilot technology.
- Cyber attacks on the functioning mechanisms of smarter and more connected autonomous vehicles might disrupt their systems and operational procedures. When this occurs, commuting stress, delayed traffic flow, collisions, accidents, and even human life loss become unavoidable.
- Tesla's advanced driving assistance system (ADAS), sometimes known as autopilot, is responsible for a number of incidents and fatalities in the United States. It has been discovered that in most situations, it shuts down roughly one second before the crash.
Car tech titans are hard at work developing natural conversational AI for vehicles, which will use speech recognition, natural language understanding, speech synthesis, and smart avatars to improve the comprehension of context, emotion, complex sentences, and user preferences. AI will be used in the future to improve vehicle safety, performance, and efficiency, as well as to address health and environmental concerns. It could also be used to build cars that communicate with one another and with other road users.
Many cars and autonomous vehicles manufacturers use their own resources to develop software, but more prefer companies that offer automotive software development services. Having experience with AI, Bamboo Apps is a great example of such a developer. To discuss your project or idea, simply book a meeting to get a free consultation and become one step closer to the vehicle of your dreams.