AI plays an essential role in the automotive industry, from advanced driver assistance systems that detect and prevent accidents to automated manufacturing processes that create car parts more efficiently. Discover more how you can harness its potential by participating in an immersive, hands-on ML/AI certification program.
Cameras and sensors used in autonomous cars rely on data annotation to detect objects within their environment, and thus’seeing’ what lies outside. This is how they operate.
1. Autonomous Driving
One key application of AI technology in cars has been autonomous driving. Autonomous vehicles use sensors and cameras to scan their environment, producing massive amounts of data which are then processed by machine learning algorithms, which determine how the vehicle should function – this may include steering adjustments, speed controls, acceleration rates or breaking mechanisms.
Multiple car manufacturers have begun experimenting with autonomous driving technology. Tesla, for instance, offers its “autopilot” feature which enables drivers to remove their hands from the wheel during traffic and on curvier roads; however, this only works at lower speeds with no guarantee that it will safely navigate curves.
Nauto has begun using smart driver systems that reduce distracted driving and help prevent collisions with other cars or pedestrians. Furthermore, these systems keep an eye on driving habits to identify unsafe practices; relaying that information directly back to drivers via dashboard displays.
Fully autonomous vehicles remain some way off; their technology requires complex computer systems that constantly evaluate and map their environment – from street infrastructure to other vehicles and even changing weather conditions – before feeding this information into an intelligent agent in the car, who then use it to build up driving experiences databases that help predict what may happen on the road.
2. Driver Monitoring
Automotive AI has found many uses within the industry, with one of its primary uses being ADAS systems like automatic braking, driver drowsiness detection and lane departure warnings being more commonly utilized than ever. These technologies utilize sensors and cameras to gather information about their surroundings before turning that data over to AI for interpretation.
Example: A camera could capture a driver’s head position and eye movements to determine what “normal” driving looks like, then, if the vehicle detects distracted activities such as eating, drinking, using their phone excessively or blinking excessively it would trigger an in-cab alert.
More forward-thinking car manufacturers have begun using artificial intelligence (AI) in their design processes. By feeding machine learning models historical and sensor data, they can quickly predict how their design choices will impact vehicle performance – saving both physical tests and development costs in the process.
SapientX has taken steps to enhance in-car voice assistants with natural conversational AI for better comprehension of context, emotion and user preferences – making virtual assistant interactions more intuitive while improving customer satisfaction.
3. Smart Parking
AI-enabled self-driving cars have transformed our driving experiences, yet not everyone can afford one of these vehicles. Internet of Things (IoT), however, makes this possible by connecting devices online – this incredible innovation has already transformed other industries such as automotive.
Smart parking systems utilize IoT sensors to collect data and predict availability of spaces, then display this information to drivers so they can find one more quickly, thus saving both time and fuel while decreasing traffic congestion due to less cars circling for available spots.
The artificial intelligence (AI) technology that drives these systems can identify parking spaces from great distances, making it safe for Level 4 autonomous vehicles to pull into spaces from any angle or drive at typical parking lot speeds. Furthermore, this system utilizes sensors, cameras and lidar (light detection and ranging; similar to radar) technology to map their surroundings quickly and take swift, nuanced decisions during complex driving situations.
4. Predictive Maintenance
Car manufacturers equip new cars with sensors that collect information about performance and components within them, along with machine learning algorithms to detect anomalies in real-time and determine which components may soon break based on past behavior and historical trends. This technology enables detection of anomalies immediately while also helping detect components which are about to fail as soon as they’ve failed before.
The system can notify the driver or vehicle manager ahead of time to prevent an impending breakdown, cutting back time spent diagnosing and fixing problems on-site and thus minimizing maintenance costs and downtime. Furthermore, this tool can optimize maintenance schedules based on individual vehicle usage patterns and conditions.
AI systems are revolutionizing the automotive industry in multiple ways. From improving driving experiences and road safety measures to streamlining supply chain processes and supply chain efficiency efficiencies. However, perhaps AI’s greatest impact lies within predictive maintenance services.
The Pentagon is also taking note of this technology. They recently awarded a startup with $1 million to test software that monitors component performance on military vehicles and predicts failures before they happen – potentially saving downtime, repair costs and unexpected outages in war zones while improving reliability and operational efficiency. As more companies develop solutions similar to this Asset Performance Management solution it will surely become mainstream.
