Human-vehicle interfaces are rapidly shifting from buttons and switches to voice control and gesture recognition. Startups that develop sophisticated voice assistants with the capacity to recognize a wide range of emotions and contexts will have an edge in competition.

AV user interfaces (UIs) can also integrate seamlessly with passengers’ biometrics for customized experiences, and patenting innovations in this field require seamless integration and real-time adaptation as the focus.

Autonomous vehicles have revolutionized the way we perceive transportation. Gone are the days when human drivers had to be physically present behind the wheel to navigate the streets. The emergence of autonomous vehicles has opened up new possibilities, one of which is the integration of gesture controls.

Imagine a scenario where you’re riding in an autonomous vehicle, and you simply raise your hand to adjust the volume of the music or lower your hand to dim the interior lights. It may sound like science fiction, but it’s a reality that has become increasingly plausible in today’s automotive landscape. In this article, we will delve deep into the concept of autonomous vehicle gesture controls, exploring what they are, their historical evolution, their associated benefits, and the challenges they bring.

Gesture controls in autonomous vehicles involving use of hand or body movements to interact with the vehicle's systems.

What Are Autonomous Vehicle Gesture Controls?

Gesture controls in autonomous vehicles refer to the use of hand or body movements to interact with the vehicle’s systems. In other words, instead of pressing physical buttons or speaking voice commands, you can communicate with your car by making specific gestures. These gestures are typically detected and interpreted by cameras, sensors, and advanced machine learning algorithms, which enable the vehicle to respond accordingly.

Gesture controls encompass a wide range of functionalities, from adjusting climate settings and audio volume to answering phone calls, navigating menus on the infotainment system, and even opening and closing windows. The primary goal is to create an intuitive and hands-free experience for passengers, enhancing safety and convenience.

Evolution and History of Gesture Controls in Autonomous Vehicles

The concept of gesture controls in vehicles is not entirely new. In fact, the idea has been around for several decades, although it’s only recently that technology has caught up with the vision. Early attempts at gesture controls were rudimentary and often impractical, as they relied on basic sensors and limited processing power.

The real breakthroughs in gesture control technology have come in the last decade, driven by advancements in camera technology, sensor precision, and the growth of artificial intelligence. These developments have allowed for more accurate detection and interpretation of gestures, making the technology viable for real-world applications.

In the automotive industry, the integration of gesture controls began with luxury car manufacturers who sought to differentiate themselves by offering innovative and futuristic features. BMW, for instance, introduced a rudimentary gesture control system in its 7 Series sedans back in 2015. This system allowed drivers to perform tasks like adjusting the volume by making specific hand movements in the air.

Since then, other automakers have followed suit, incorporating more advanced gesture control systems. Companies like Tesla, Mercedes-Benz, and Audi have integrated gesture controls into their vehicles, adding to the growing popularity of this technology.

Benefits and Challenges of Gesture Controls

The adoption of gesture controls in autonomous vehicles comes with a range of benefits and challenges.


Enhanced User Experience

Gesture controls offer a more intuitive and natural way of interacting with the vehicle’s systems. Users can simply point, swipe, or make specific gestures to achieve desired actions, making the driving experience more user-friendly.


By reducing the need for physical button presses or touchscreen interactions, gesture controls contribute to safer driving. Drivers can keep their hands on the wheel and their eyes on the road while making adjustments or accessing information.


Gesture controls can be a game-changer for individuals with physical disabilities who may find traditional controls challenging. It offers a new level of accessibility in automotive technology.

Aesthetic Appeal

Gesture controls can create a clean and minimalist interior design. The absence of physical buttons and knobs lends a modern and sleek look to the vehicle’s cabin.


Learning Curve

Users may need time to get accustomed to the specific gestures required for various functions. If not designed well, this learning curve can be frustrating and counterproductive.

Accuracy and False Positives

Gesture recognition technology must be highly accurate to avoid misinterpretations of gestures and false positives. It can be frustrating for users if the system misinterprets their intended actions.

Visibility and Lighting Conditions

Gesture control systems heavily rely on cameras and sensors, which can be affected by low light conditions, glare, or obstructions. These challenges need to be addressed to ensure consistent performance.

Privacy Concerns:

Gesture controls involve cameras capturing movements within the vehicle. Privacy concerns may arise if users feel uncomfortable with the idea of being monitored, even if it’s for benign purposes.

Gesture-Based Controls

Gesture-based control systems take input from your body rather than traditional keyboard, mouse and game controller input methods, providing more intuitive and immersive user experiences. Additional haptic feedback may further improve user experiences.

The technology works by recognizing hand movements or other gestures made by an individual and translating them to commands, similar to voice recognition technology used for simple actions like opening apps or sending emails. This may be accomplished using infrared sensors, depth cameras, or other sensors that detect both hand position and speed.

Gesture-based controls are becoming more and more mainstream across consumer and commercial products. Public restrooms now often come equipped with gesture-controlled sinks, air dryers, and paper towel dispensers for improved hygiene, helping prevent germ spread – particularly useful during flu season. Healthcare can use similar technology to decrease healthcare-associated infections while increasing patient safety and modernizing surgical procedures.

Piccolo Labs has developed a smart home system that enables users to give Alexa a break by using body movements as the controlling mechanism of various devices and activities. Their technology uses motion tracking, pose estimation, 3D reconstruction, and deep learning algorithms to quickly recognize people and gestures – it even detects subtle movements from patients suffering from tremors or Parkinson’s Disease to turn these into commands for treatment purposes.

BMW has developed gesture control into some of their vehicles’ infotainment systems to increase driver safety and convenience. The technology enables drivers to change radio stations, air conditioning settings, and music playlists with just a wave of their hands instead of fiddling with buttons and touchscreens while driving.

Gestural recognition technologies currently require significant computing power in order to filter out noise caused by random movements that don’t serve as control signals, leading to systems only available on luxury or Tier 1 automobiles and costly to maintain over time. Many disruptive solutions exist that aim to lower both the cost and complexity of this technology so it becomes accessible to a broader range of consumers.

Augmented Reality (AR)

Augmented reality (AR) overlays digital information onto a user’s real-world view of their environment, such as 3-D images of furniture in their home to help with purchasing decisions or step-by-step instructions for complex machinery operations. AR can also use computer vision scanning technology to recognize objects for more natural interactions with virtual interfaces.

Under today’s economic circumstances, AR has gained more and more traction. AR can reduce training and upskilling costs, improve workflow, promote remote collaboration, and reduce manufacturing costs by eliminating physical interfaces.

Companies need to determine how best to utilize all of the capabilities that AR provides and how these might fit into their overall business strategy. Companies wanting to develop AR internally may need to hire and train appropriate talent, while those wanting turnkey solutions for specific business requirements could partner with best-of-breed technology and services companies that provide turnkey solutions.

Simple AR applications might include creating digital product representations and linking them with PLM systems so employees always have access to the latest data. More advanced experiences may require advanced digital modeling or digitization techniques; in addition, these experiences should connect to multiple data sources to enable real-time business intelligence applications and provide access to other valuable sources of information.

AR is defined by its ability to accurately overlay digital information onto physical surroundings. One approach uses markers as anchor points, making the experience relevant for specific objects or locations – for instance, vehicle head-up displays that present navigation and collision warning information directly in a driver’s line-of-sight are one example; more complex devices for factory workers must recognize physical surroundings using GPS, accelerometers and cameras as well as shape recognition algorithms in order to display relevant data.

The 2010s saw tremendous advances in AR design as sensors became more powerful, affordable and mainstream. Pokemon GO became one of the most widely-used AR applications with animated characters appearing within users’ environments using mobile phone cameras; Google introduced stickers that can be dropped onto images of real world scenes for AR users to enjoy.

AR using computer vision scanning technology to recognize objects for more natural interactions with virtual interfaces.

Haptic Confirmation

Touch is an immensely potent sensory modality, capable of communicating information about the environment, objects and people like no other modality can. When used in combination with gesture control, haptics creates an interactive non-visual user interface – surface haptics (such as on touchscreens), mid-air haptics such as Ultraleap’s touch feedback technology or touch feedback pads can all serve to convey this data more directly – providing for more subtle ways of interaction without screens, buttons and knobs being needed for interaction.

Not only can haptics enhance user experiences, but it can also increase accessibility for those with visual or hearing impairments. Haptics is used to send vibrations when an input is accepted or an error occurs – something audio/visual notifications alone would never do. Warnings delivered through haptics are typically delivered 1.7 times quicker and harder for people to ignore than audio-based notifications alone; making them particularly beneficial in medical applications where this method helps reduce patient overdosing in hospitals.

Haptics offer drivers a safer driving experience by encouraging them to keep their eyes on the road instead of their screens. Studies show that using gesture control + mid-air haptics together can reduce glances away from the road by 39% compared to using touchpads and buttons on modern infotainment systems alone. This may significantly lessen driver distraction and fatigue.

Context metadata can be translated to one or more haptic parameters, including, but not limited to magnitude, frequency, duration and waveform. Once converted, these signals can be sent directly to an actuator for confirmation haptic effects; additional signals may also be repeated at regular intervals or randomly.

Haptics can be used to confirm when buttons have been selected on a touchpad or to alert a user when their hand has come near a virtual radio dial. They can also confirm input has been recognized or provide status updates about devices or software.

Machine Learning (ML)

Machine learning (ML) is one of the most impressive and cutting-edge technologies of our era. As a form of artificial intelligence, ML enables machines to learn from raw data without human programming – in an iterative process to find patterns or behaviors which would otherwise be difficult or impossible for humans to discover. As such, its use can improve product development, increase marketing efficiency, and boost operational productivity.

Business of all sizes can utilize artificial intelligence (AI). AI can be applied to numerous business processes, including customer service, automated data entry, fraud detection and predictive maintenance. Furthermore, companies could utilize it to create personalized experiences and custom content – for instance developing a chatbot that answers customers’ inquiries will reduce operating costs significantly.

ML can also help businesses predict and prevent problems before they arise, by analyzing data sets to spot patterns that might indicate when specific equipment or parts may break down – this enables businesses to schedule preventive maintenance ahead of time, saving both money and repair bills. Furthermore, machine learning helps detect security threats more rapidly, making this an indispensable function in today’s digital environment of cyber attacks and vulnerabilities.

Machine learning technology is being integrated into an ever-expanding array of business apps and software, including search engines, spam filters, malware detection tools, online banking apps and voice assistants. Furthermore, AI can help automate tasks and free employee time in manufacturing companies where manual processes take up an excessive amount of employee resources.

Machine learning (ML) has already found applications across numerous sectors and is projected to become even more widespread over time. This field includes self-driving cars as well as online recommendations, advertisements and social media algorithms such as recommendations found in e-commerce, advertising and social media networks; it is also utilized as part of real estate valuation services, language learning programs and medical imaging software services.