Aside from electric vehicles, AI-powered self-driving cars are seen as the next big thing in the automotive industry.
Through a mix of sensors, machine learning algorithms, and cloud-based systems, AI can make cars smarter, more responsive, and even better equipped to handle complex traffic scenarios.
And so it has the potential to save lives and make driving more convenient and accessible—and perhaps even reshape how we use vehicles in its entirety.
In this article, we’ll dive into the different ways AI is innovating cars.
Key AI Safety Features in Vehicles
While many of these features are not fully implemented in most vehicles out on the road today (as most of them are a couple of years old), newer vehicles in the market are including more and more of these AI features.
1. Advanced Driver-Assistance Systems (ADAS)
ADAS is a suite of AI-powered tools designed to support drivers in making safer driving decisions. These systems vary. Some cars offer fully autonomous driving, while others only assistance to human drivers.
Common components of ADAS include:
- Adaptive Cruise Control (ACC): This system adjusts the vehicle’s speed based on the distance from the car ahead, ensuring that there is a safe between the vehicles.
- Lane-Keeping Assist (LKA): Cameras and AI algorithms detect lane markings, automatically steering the car back into its lane if it starts to drift.
- Automatic Emergency Braking (AEB): Sensors and AI can detect imminent collisions based on distances between cars and automatically apply brakes to prevent or minimize impact.
These features operate in real time and often intervene faster than natural human reflexes.
2. Blind Spot Monitoring and Collision Avoidance Systems
Blind spots are some of the most common causes of accidents, especially during lane changes or overtaking scenarios.
AI-powered sensors and cameras—which often view all of the car’s surroundings—can monitor areas outside the driver’s direct view. These systems can then detect vehicles in blind spots and alert drivers about them. Or even brake entirely as with AEB.
More advanced systems, like LiDAR (Light Detection and Ranging) which uses light sensing, can analyze the speed, distance, and trajectory of all objects around the car. They can then alert the driver or steer or brake themselves to avoid collision.
3. Driver Monitoring Systems
But there are also cameras inside the car. And with AI, they can be used to ensure that drivers remain attentive (or awake) behind the wheel.
With facial recognition systems, they can detect signs of fatigue, distraction, or impairment. If the system senses that the driver is unfit to drive, it issues warnings or even takes control of the vehicle in critical situations.
4. Self-Parking Features
These systems and technologies can also help with parking. After all, any new driver knows that parking can often be harder than driving on the road!
While many cars nowadays offer parking assistance systems (like showing an overhead diagrammatic view of the car and its surroundings), imagine fully self-parking systems!
Difficult parking situations (like parallel parking or navigating tight spaces) will now be a breeze—and therefore be much safer too.
5. Predictive Maintenance
Beyond real-time accident prevention, AI is helping vehicles remain safe through predictive maintenance. After all, poor car maintenance often leads to accidents, too!
AI can now analyze things like engine performance and tire conditions to ensure that the car is fit for the road. AI systems can then alert its drivers (who, as we know, often forget to check) of potential mechanical failures before they even occur.
The Rise of Autonomous Vehicles
But what about fully autonomous vehicles (AVs)?
Companies like Tesla, Waymo, and General Motors have been leading the charge in developing self-driving cars.
These AVs rely on AI systems to interpret vast amounts of data collected from their surroundings to navigate complex environments without human intervention.
Key technologies in AVs include:
- Simultaneous Localization and Mapping (SLAM): A method that allows vehicles to create a detailed map of their driver’s common environment (like their route from home to work). This lessens the time needed for AVs to fully take in the car’s surroundings, making it more responsive.
- Deep Learning Algorithms: These algorithms enable vehicles to recognize objects, such as pedestrians, traffic lights, and road signs, with remarkable accuracy. This is true even at night, when human vision can be impaired through advanced systems like LiDAR or thermal sensing.
- Vehicle-to-Everything (V2X) Communication: AI is used in V2X, systems that allow cars to exchange data with other vehicles, infrastructure, and even pedestrians. This allows them to collaborate to ensure mutual safety.
However, AVs are far, far from being widespread yet. In fact, around two-thirds of US drivers still don’t trust self-driving cars. This is a testament to how new—and potentially concerning—this technology still is.
After all, there are still numerous challenges with AI, as we discuss below.
Challenges and Ethical Considerations
Despite its benefits, the deployment of AI safety features is not without challenges. Key issues include:
- Data Privacy: AI systems rely on people’s data—and so many people are concerned that the further proliferation of AI will require more and more of their data to be needed for analysis. Some people say that IP VPN solutions must be included in these cars to help protect the driver’s privacy.
- Insufficient Datasets: All sorts of things can happen on the road, and AI systems must be trained on diverse datasets to ensure they function effectively in all scenarios. If AI is trained on limited datasets, it might fail to function properly in unique scenarios, leading to accidents.
- Legal and Ethical Questions: In the event of an accident with AVs, determining who is liable for the incident will be a complex issue. Governments are already constantly changing laws about autonomous vehicles—and will likely continue to do so as the technology continues to rapidly evolve as well.
Addressing these challenges requires collaboration between automakers, regulators, and technology companies.
Conclusion
AI is still in its infancy, which explains why many people are still reluctant about its application in cars. After all, lives are on the line when it comes to driving.
Studies by the Insurance Institute for Highway Safety (IIHS), state that there is still “little evidence” that partial automation prevents crashes.
However, little is better than nothing. There is arguably still great potential in using AI to make our roads safer for everybody: drivers, passengers, and pedestrians. So, the work to tap into AI’s great potential fully is far from over.
What could the future hold? Augmented reality dashboards? AI-powered city traffic management systems?
The future of cars has never been more intriguing.
