Most electric motorcycles still behave like motorcycles first and utility machines second. They chase performance numbers, oversized displays, or aggressive styling while ignoring a simple reality: most urban riders just want something practical enough to replace short car trips. The ANY LUV1 approaches the problem differently. Instead of behaving like a sportbike with batteries attached, it feels more like a compact urban tool designed around everyday life.

Created by Belgian startup ANY Mobility, LUV1 is sandwiched somewhere between an electric scooter, cargo bike, and lightweight motorcycle. The company calls it a “Life Utility Vehicle,” and the name makes sense once you look beyond the styling. Nearly every part of the vehicle revolves around usability, whether that means carrying groceries, office gear, camera equipment, or handling the kind of short-distance errands people usually default to using a car for.

Designer: ANY Mobility

That practicality starts with its packaging. The integrated cargo compartment offers 120 liters of storage, which is significantly more useful than the tiny under-seat compartments found on most scooters. It is large enough to carry shopping bags, delivery equipment, or a backpack and helmet without forcing riders to strap everything externally. Front and rear cargo racks expand that flexibility further, while configurable dividers allow owners to organize storage depending on the task at hand.

The modular approach is where the concept becomes genuinely interesting. Instead of locking owners into one fixed setup, the LUV1 can be customized with interchangeable body panels, seating layouts, storage accessories, and optional weather protection. One configuration can prioritize cargo hauling during the week while another leans toward casual commuting on weekends. It follows the same logic that made modular furniture and adaptable workspaces appealing: people increasingly want products that evolve with their routines rather than forcing routines around the product.

Visually, the bike avoids the exaggerated “future mobility” look many startups lean on. The clean bodywork and restrained surfacing come from Granstudio, the Italian design firm led by former Pininfarina design director Lowie Vermeersch. That design pedigree shows in the proportions and detailing. Even functional components like the storage compartments and structural frame feel integrated into the design rather than added as an afterthought.

Underneath the bodywork sits a modular aluminum chassis produced using high-pressure die-casting, a manufacturing method more commonly associated with larger automotive companies. The setup helps reduce complexity while providing the platform with enough flexibility to support various accessories and future configurations. Power comes from an 11 kW rear hub motor paired with dual swappable lithium-ion battery packs totaling 6.5 kWh. ANY Mobility claims a range of 68 to 87 miles, depending on use, while the top speed is rated at 62 mph, making the bike suitable for both dense city streets and suburban commuting. Charging the batteries through a standard 220V outlet reportedly takes under four hours.

The LUV1 also keeps accessibility in mind. It weighs around 352 pounds and features a relatively approachable 30.9-inch seat height, making low-speed maneuvering less intimidating for newer riders and shorter commuters alike. According to reports, the company expects pricing to fall between €7,000 and €10,000 (approximately $8,150 – $11,600) depending on configuration, and reservations have already opened ahead of production plans.

What makes the ANY LUV1 stand out is not raw performance or futuristic gimmicks, but how realistically it understands modern urban mobility. Most people are not looking for an electric motorcycle to replace weekend entertainment. They are looking for something convenient enough to replace unnecessary car usage, and the LUV1 feels designed precisely around that idea.

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Every sensor on a Waymo robotaxi sees the world in layers. The LiDAR maps it in three dimensions, radar bounces through it, and cameras read it in color and contrast, building a composite picture of the road that no human retina could match at the same fidelity. So when a Waymo encountered a flooded section of a 40 mph road in San Antonio on April 20, the car absolutely saw the water. It slowed down for it. Then it drove in anyway, floated off the road surface, and came to rest in Salado Creek. The voluntary recall Waymo filed with NHTSA on April 30, covering 3,791 vehicles, was triggered not by a sensor that missed a hazard, but by a software stack that saw the hazard clearly and still chose the wrong response.

You might be sitting in one of those 3,791 recalled vehicles right now, somewhere in Phoenix, Los Angeles, Austin, or Atlanta, and Waymo has confirmed the permanent software fix is still in development. Tesla’s Cybercab, entering production at Giga Texas, runs a supervised robotaxi service in Austin, Dallas, and Houston on a pure-vision architecture with no LiDAR whatsoever. Uber’s platform in Dallas is dispatching Avride-operated Hyundai Ioniq 5s that are currently under NHTSA investigation for 16 crashes involving lane changes and failure to stop for traffic ahead. Amazon’s Zoox uses cameras, LiDAR, radar, and long-wave infrared on every vehicle, the most sensor-redundant consumer-facing stack in the industry, and is still in limited city testing. Each platform has a different answer to what a self-driving car should do when it encounters something it cannot traverse, and after the San Antonio creek, all of those answers deserve a much closer look.

The NHTSA recall notice characterizes the flaw precisely: the software “may allow the vehicle to slow and then drive into standing water on higher speed roadways.” That is a classification error buried in the decision stack, not a sensor failure, and the distinction matters more than the recall number suggests. Waymo’s 5th-gen Jaguar I-Pace and 6th-gen Zeekr RT both carry LiDAR, radar, and cameras in overlapping fields of view, and the San Antonio car processed the flooded road accurately as a hazard worth responding to. The decision architecture, however, had no hard-stop condition for water on a 40 mph road, only a caution flag that reduced speed and left proceeding as an available output. A separate Waymo had already been stranded near McCullough Avenue in San Antonio roughly two weeks before the April 20 incident, confirming this was a repeatable failure mode across a fleet that was still carrying passengers in nine other cities.

Tesla’s Cybercab carries no LiDAR, putting its supervised fleet in Austin, Dallas, and Houston in a fundamentally different position when floodwater appears than Waymo’s overlapping sensor stack would. The platform relies on eight cameras and 4D millimeter-wave radar, meaning no independent depth-sensing channel exists to assess water severity when camera visibility degrades in heavy rain. A real-world FSD 14.3.1 test in April 2026 ended in manual takeover when the front bumper camera submerged, a precise illustration of where the vision-only approach runs out of information. Avride, dispatching Hyundai Ioniq 5s through Uber’s Dallas app since December, is under concurrent NHTSA investigation for 16 crashes involving lane changes and failures to stop for road hazards, all 16 occurring with a trained safety monitor seated in the vehicle. Amazon’s Zoox sits at the opposite end of the sensor redundancy spectrum, combining cameras, LiDAR, radar, and long-wave infrared in a 360-degree array with a human TeleGuidance fallback for scenarios the stack cannot resolve, though its commercial footprint remains a fraction of Waymo’s.

The Waymo recall, the Avride probe, and a dashcam video of a Waymo rolling through a red light on Irving Boulevard in Dallas all surfaced in the same seven-day window, collectively mapping the same design gap across three platforms: a perception-to-action pipeline that detects a hazard but generates the wrong response to it. Waymo’s OTA patch is deploying now, but the permanent fix remains in development, meaning every current ride runs on interim constraints rather than a finished solution. The San Antonio incident involved an empty car, and that is the only reason this story ends with a recovery operation rather than a casualty report. Each platform carrying passengers today is still writing its edge-case rulebook, publishing each new chapter only after something breaks on a live road. Knowing which system you are riding in, what its sensor stack can assess in a sudden storm, and whether its flood-detection logic has been patched from an interim fix to an actual solution is, I’d argue, the most practical safety question a passenger can ask in 2026.

The post Waymo’s Self-Driving Car Saw the Flood and Drove In Anyway. Here’s The Problem Plaguing Every Robotaxi. first appeared on Yanko Design.