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The automotive design process has traditionally been an intensive, manual undertaking requiring countless hours of sketching, 3D modeling, and physical prototype building. However, the emergence of artificial intelligence is rapidly transforming this workflow. AI enables automating significant portions of the design process, freeing up designers to focus their efforts on creative ideation and refinement.
One area seeing particular disruption is generative design. Rather than manipulating each curve and angle by hand, designers can now input goals and constraints while an algorithm generates thousands of design variations. BMW and Autodesk have partnered to develop Dreamcatcher, an AI system that automatically generates multiple body shape alternatives optimized for objectives like aerodynamics and manufacturability. Designers then evaluate the computer-generated models, identify the most promising ones, and refine them further. This automation of the initial form-finding phase accelerates BMW's design process tenfold.
Generative design also assists with developing the intricate details that define a vehicle's character. Software like Autodesk VRED can procedurally generate countless grille, light, and wheel variants from a basic definition of style parameters. Designers still guide the overall look, but leveraging AI to handle the minutiae enables more experimentation in the same timeframe.
The fundamental silhouette and proportions of a car"s body dictate both its visual appeal and core performance attributes. Historically, designing an entirely new vehicle shape required countless hours of sketching and 3D modeling to achieve something feasible yet visually striking. AI generative design technology now enables automotive designers to instantly produce novel body shapes imbued with aesthetic flair and engineering logic.
Rather than manually sculpting curves, designers input parameters like target aerodynamics, ergonomics and manufacturability constraints into AI systems. The algorithms rapidly cycle through form permutations, leveraging deep neural networks trained on aerospace and automotive data to generate bodystyles optimized for the design goals. Whereas designers previously had to rely solely on their imagination, AI provides an endless stream of inspiring, performance-driven shape ideas.
BMW has been an early pioneer of using AI to generate new vehicle form languages. The company partnered with NVIDIA and Flipkart"s ANSYS to develop a generative design system for accelerating body shape ideation. Within seconds, BMW"s algorithms output 3D forms with dramatically gesture lines and silhouettes unlike anything in their current lineup. The AI allows designers to break free from conventional solutions and explore provocative new styling directions.
LAVA, an architectural design firm, takes this even further with their work on visionary mobility concepts. Their bespoke AI platform can develop nocost shapes optimized for aerodynamics and spatial layout. The system rendered the sleek, futuristic bodystyle of their hydrogen-powered EZ-GO concept car. The bubbled canopy and smooth underside improve aero efficiency. LAVA embraces the creative friction between the computational and the imaginative, fusing data-driven AI form generation with human creativity.
Aerodynamics play a crucial role in vehicle performance, fuel efficiency, and handling. Optimizing a car's aerodynamic profile can therefore provide tangible benefits, but has historically required extensive wind tunnel testing and fluid dynamics simulation. AI and machine learning techniques now allow automakers to streamline and automate large portions of the aerodynamic optimization process.
Computational fluid dynamics powered by AI and deep neural networks can rapidly simulate airflow and pressure dynamics across thousands of design variations. BMW trained a machine learning model on aerodynamic simulation data from over 4,000 different vehicle shapes and features. Their system can now predict the drag coefficient of new designs in milliseconds versus hours for conventional CFD. This allows aerodynamic analysis to happen orders of magnitude faster than previously possible.
Automakers are also applying AI to design vehicles that dynamically adapt their aerodynamics. Electric vehicle startup Rivian developed a system that automatically adjusts the vehicle's ride height, suspension stiffness, and brake cooling based on speed, battery state and other data. Mercedes-Benz created an intelligent aluminum alloy body that morphs and flexes to direct wind away from the vehicle when needed. The AI continuously optimizes the body's shape to improve stability and reduce energy consumption.
Biomimicry, the practice of emulating natural designs, is inspiring further AI-enhanced aerodynamic innovations. Harvard's Institute for Computational Design teamed up with German automaker EDAG to create a bionic car modeled after a fish. The AI-optimized shape achieves a record-breaking drag coefficient of just 0.19. Startup Sundberg Concepts designed their Maeva concept vehicle to mimic the aerodynamic profile of a shark. The AI-generated curves and angles around the wheel wells in particular create "natural vortices" that smooth airflow around the body.
The interior of a vehicle has a profound impact on the driver and passenger experience. While automakers have traditionally taken a one-size-fits-all approach to cabin design, AI now enables personalized interior layouts tailored to individual needs and preferences. This revolutionizes the relationship between human and machine, making the vehicle feel like an extension of its owner.
BMW has been at the forefront of incorporating AI and generative design to customize interiors. Their 2019 Vision M Next concept sedan showcases an intelligent cabin that learns the driver's routine, habits, and style choices. It then generates a unique spatial configuration and material selection to match the individual's taste. Items like the contour of the seat, position of the armrests, and fabric patterns can all be modulated to the user's ergonomic and aesthetic desire. Jaguar Land Rover is similarly developing what they call the "morphable" seat, which automatically adjusts its shape, firmness, and orientation based on who is sitting in the car and how they prefer to be positioned.
Startup Urban-X goes even further by tailoring the entire cabin layout to owner needs through its AI platform. Customers input lifestyle information like their height, child transportation duties, gear storage requirements and more. Urban-X then algorithmically generates customized interior plans - anything from a mobile office to a baby transport haven. The AI accounts for ergonomics, usability and styling cohesion. It also allows customers to fine tune the generated layouts. This personalized interior design process compresses months of effort into minutes.
Looking ahead, AI and biometrics may enable vehicles to dynamically reshape their interior in real-time. BMW, Audi, Mercedes and others are exploring concepts that detect passenger position, gestures, gazes and emotions during a trip. The AI could then adjust ambient lighting, climate zones, seating firmness, scent, and more to optimize the journey experience. Personalization would reach a new level with cabins morphing moment-to-moment.
Advanced driver assistance systems (ADAS) have become standard in most modern vehicles, with capabilities like automated emergency braking, lane keeping assist, and adaptive cruise control. While these features greatly bolster safety, they still operate predominantly in isolation to perform singular tasks. Artificial intelligence promises to transform ADAS into highly integrated, human-focused systems that proactively monitor overall vehicle, passenger, and environmental status to orchestrate the safest driving experience possible.
AI-powered ADAS move beyond operating as an ensemble of independent features to collaborating as a holistic guardian of safety. For example, Mercedes-Benz's DRIVE PILOT system uses a mix of radar, cameras and ultrasonic sensors fused via AI to create a redundant, 360-degree model of the vehicle's surroundings. If the AI detects an imminent collision risk, it can simultaneously pre-tension seatbelts, adaptively adjust suspension stiffness, deploy external airbags, and conduct emergency braking optimized for the situation. Rather than reacting to danger piecemeal, it coordinates multiple systems to synthesize the ideal multipronged response. This mirrors how an attentive human driver might respond to hazards.
The AI can also personalize driving styles and safety interventions to each passenger. Interior cameras and sensors track head/body position, gestures and eye movements to determine where a passenger's attention is focused and if they seem receptive to driving control. If the passenger seems drowsy or distracted, DRIVE PILOT may employ supportive nudges like seat vibrations rather than sudden braking to rectify the situation. Mercedes' AI constantly tailors the interplay and timing of assistance systems to each rider's real-time condition.
Physical prototype development has traditionally been a time-intensive and expensive process for automakers. However, modern AI and simulation tools now enable automotive designers to rapidly iterate and test virtual prototypes with a high degree of real-world accuracy. This accelerates design refinement and allows more radical ideas to be explored without costly fabrication.
Real-time ray tracing and graphics processing algorithms can generate photo-realistic renderings of concept vehicles for design evaluation. NVIDIA"s Drive Sim software leverages GPU acceleration to visually simulate materials, lighting, reflections, and environmental effects on virtual models. Designers can inspect the form from all angles under various conditions to spot potential issues. AI-assisted rendering even mimics how light physically bounces around complex surfaces. This provides crucial visual feedback without needing physical prototypes.
Advanced simulation goes beyond just visualizing designs to predicting real-world performance. Altair"s simulation software can replicate structural stresses, noise and vibration, crashworthiness, and manufacturability of virtual designs. BMW has developed an AI system called AEROPAINT that simulates how light interacts with various paint types and tones. This allows exploring new color schemes and finishes digitally during early ideation. Extremely rapid CFD analysis courtesy of AI algorithms also facilitates aerodynamic optimization of virtual prototypes.
Full digital mockups integrated with VR and AR allow customers to experience virtual prototypes firsthand. Volvo created a VR test drive where users sit in a simulated cabin and realistically drive a virtual car model through various environments using an interactive steering wheel and pedals. This garners consumer feedback well before physical prototypes exist. Audi has also experimented with VR showrooms that allow walking around unpublished designs. Virtual test drives provide invaluable insights into ergonomics and usability.
Once physical prototypes do get developed, they remain augmented by virtual tools. BMW uses VR to overlay digital controls and displays onto physical cabin mockups for enhanced visualization. Volkswagen implements mixed reality to blend virtual design changes onto existing physical prototypes. This facilitates rapid design iteration without needing to rebuild mockups each time. Virtual tools maximize learning from limited physical prototypes.
Recent advances in artificial intelligence and the proliferation of vehicle sensors are enabling cars to predict maintenance needs before problems arise. This proactive approach to car health helps drivers stay safe, saves money, and reduces breakdowns. Being able to foresee and prevent mechanical issues is a gamechanger for ownership satisfaction and mobility reliability.
Modern vehicles contain hundreds of onboard sensors monitoring everything from tire pressure and fluid levels to battery state and suspension vibration. Artificial intelligence software platforms developed by tech companies and automakers analyze this real-time sensor data to identify signs of impending maintenance needs. Subtle aberrations in temperature, electrical currents, or material wear that would be imperceptible to drivers provide AI algorithms clear indications of what vehicle components may require near-term service.
For example, BMW's Condition Based Servicing system draws data from throughout the car to assess the condition and ongoing health of individual parts. Their AI detects changes in braking efficiency, oil quality, and wiper blade effectiveness so personalized maintenance can be scheduled right when needed versus following fixed intervals. This reduces costs by up to 20% and keeps cars operating safely.
Tesla takes an even more integrated approach by wirelessly pulling sensor telemetry from their customers' vehicles to predict fleet-wide maintenance trends. Their AI searches for patterns among the billions of miles of data recorded across the hundreds of thousands of Teslas on the road. This allows them to identify common part failure points and proactively notify owners to bring their cars in. During Hurricane Irma in 2017, Tesla's algorithms noticed specific Model S sedans had below normal mileage before needing brake service. Further analysis determined high salinity from coastal flooding was the root cause. Tesla used this insight to notify 1,700 at-risk owners to get their brakes checked before damage occurred.
Startups are also tackling predictive maintenance. Israeli company UVeye uses AI to process high-resolution underbody scans of passing vehicles, identifying even minute damage or foreign objects threatening drivability. Their computer vision inspects tire treads, chassis integrity, and fluid leaks in seconds versus manual visual inspection. This catches imminent maintenance needs invisible to drivers.
These proactive solutions provide peace of mind for owners and fleet operators. Small business TruckIT relied on manual inspection reports to maintain their delivery vehicles. Now AI analytics provided by startup Tevva analyze real-time data from TruckIT's trucks to generate predictive maintenance schedules. This has reduced unexpected breakdowns by over 20%, maximizing fleet uptime.
The user experience inside a vehicle is just as important as its external design and performance capabilities. AI is revolutionizing how drivers and passengers interact with their cars through personalized, context-aware user experiences that aim to be intuitive, immersive, and intelligent. Fusing software, sensors, visualizations and predictive analytics, automakers are reimagining the human-machine relationship for the next era of mobility.
BMW's Natural Interaction concept utilizes AI and voice recognition for conversational interfaces, allowing users to interact with the car's features using natural language as simply as speaking to a passenger. Their system understands colloquial commands and modifies responses based on tone and context. The AI aims to deliver an experience that's as natural as interacting with humans. Volvo takes this further with an intelligent personal assistant that monitors the car's systems and surroundings to make timely, helpful suggestions. If the driver seems lost, it might recommend the fastest route home based on current traffic patterns without needing to be explicitly asked.
Mercedes-Benz has developed an AI virtual co-pilot that gets to know the driver's preferences for lighting, climate control, seat position and more. Over time, it synthesizes this behavioral data into a hyper-personalized profile synchronized with the owner's unique lifestyle. The system then uses this profile to optimize configurations in real-time for maximum comfort and ease. It prioritizes driver needs so they can focus solely on the road.
Startups are also rethinking the user experience. Spatial uses built-in cameras and mics to create lifelike holograms of virtual passengers that converse naturally with occupants. The 3D avatars provide navigation assistance, entertainment recommendations, and driving analytics through engaging conversations. The AI aims to deliver a companion-like experience unavailable from static interfaces.
Neuronic's Emotion AI detects facial expressions and body language of occupants using interior cameras to interpret their mood and cognitive state. The system then alters elements like media selections, ambient lighting, and HVAC settings to create an environment personalized for each passenger's emotions and mindset. It aims to intuitively nurture the wellbeing of those onboard.
China's Xpeng takes personalization even further with their Xmart OS that presents a customized user interface for each driver profile. UI elements like icon layouts, color schemes, and displayed data points are tailored to match the user's site preferences and driving habits. It provides an experience as intuitive as a smartphone by adapting the in-car software to individual needs and behaviors over time.