Robotaxis Will Start for Real in 2026: Europe’s Low‑Grip Safety Gap and the Easyrain Fix

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From Rain to Robotaxis: Why Europe’s 2026 Launch Needs a Low-Grip Safety Revolution

From London to Berlin, 2026 is shaping up as the year robotaxis move from pilot projects to paid, passenger‑carrying services. Policy momentum in the United Kingdom, an updated Level‑4 legal framework in Germany, and new industry alliances — notably Lyft teaming with Baidu — are setting the stage. Yet one unresolved hazard could limit scale and public trust: low‑grip conditions caused by heavy rain, standing water, snow and ice. This article reviews what’s coming in 2026, why adverse weather still defeats today’s autonomy stacks, and how Easyrain’s DAI, AIS and ERC systems can make robotaxis safer in Europe’s toughest climates.

2026: Europe’s Robotaxi Moment

The UK’s new legal framework for self‑driving services clears the way for paid operations as early as 2026, according to the government’s own roadmap (gov.uk). Germany, for its part, already permits Level‑4 operations in defined areas under its national law, enabling commercial services once operators secure approvals from federal and local authorities (BMDV).

Against that backdrop, ride‑hailing platforms are aligning with autonomy specialists. Lyft plans to deploy Baidu’s fully electric Apollo RT6 robotaxis in the UK and Germany starting in 2026 (pending regulatory sign‑off), building on its European footprint following the acquisition of FREENOW (Reuters; Lyft). Uber continues to expand AV integrations globally across multiple partners, while Waymo scales U.S. robotaxi service areas and Tesla explores ride‑hailing and autonomy initiatives.

Why Low‑Grip Is the Achilles’ Heel of Autonomy

Today’s autonomous driving stacks are excellent at perception and planning in fair weather. But they degrade when friction falls and visibility suffers. Cameras lose lane contrast under spray; lidar returns are attenuated and cluttered by rain and snow; radar is robust to precipitation but can lack spatial resolution. Critically, most stacks do not estimate available tire‑road friction in real time with the precision needed to adapt speed, following distance, and braking before grip collapses — especially on water‑logged surfaces that can trigger aquaplaning.

Independent testing underscores the gap. In controlled studies of production driver‑assistance systems, moderate to heavy rain significantly increased lane departures and collision rates, even at suburban speeds — a warning flag for any autonomy stack that inherits similar sensing and control assumptions (AAA). Regulators also continue to scrutinize AV performance in poor visibility and low‑adhesion scenarios, emphasizing that safe operation must extend beyond blue‑sky ODDs (NHTSA).

Europe’s climate compounds the challenge. Compared with Sun Belt U.S. cities, many European metros see frequent heavy rain, standing water, icy mornings, plowed‑snow residue, polished cobblestone, and road paint with variable micro‑texture — all of which reduce μ and confuse sensors. Without reliable prediction of low‑grip pockets ahead (puddles, black ice, slush), robotaxis risk late interventions, longer stopping distances, and emergency maneuvers on surfaces that may no longer support them.

A Practical Safety Path for 2026: Easyrain’s DAI + AIS + ERC

If Europe wants robotaxis that work year‑round, autonomy needs a dedicated low‑grip safety layer that predicts, prevents, and shares risk in real time. Easyrain’s technology suite provides that layer in three parts:

1) Predict the risk — DAI (Digital Advanced Information)

DAI is a family of predictive virtual sensors that infer road‑tire conditions from standard vehicle signals plus on‑board perception. It delivers an “early warning” μ‑risk map to the autonomy stack so it can adapt before grip collapses. Key modules include:

  • Aquaplaning: anticipates water‑film hazards in ruts and puddles to trigger speed and trajectory adaptations.
  • Snow & Ice: detects winter low‑μ conditions where cameras and lane markings are unreliable.
  • Ground: classifies surface types and micro‑texture to refine friction forecasts.
  • iTPMS & Tire Wear: monitor inflation and wear to keep tire performance within the ODD guardrails.
  • Wheel Misalignment: catches alignment issues that degrade stability and braking on slick surfaces.

2) Prevent the loss — AIS (Aquaplaning Intelligent Solution)

When heavy wet conditions appear, AIS provides an active safety intervention to defeat aquaplaning — a scenario where ABS/ESC cannot restore grip because the tire rides on a water film. AIS is engineered to remove the water film just ahead of the contact patch, restoring the tire‑road contact needed for steering and braking. For robotaxis, AIS adds a deterministic safeguard in the worst rain events that perception‑only stacks cannot reliably overcome.

3) Share and scale — ERC (Easyrain Cloud)

ERC aggregates vehicle‑borne low‑grip detections (from DAI/AIS and fleet signals) to build a live, privacy‑preserving risk map. This connected layer lets operators reroute fleets away from emerging aquaplaning hotspots, issue speed caps per road segment, and feed predictive analytics back into autonomy planning. As ERC density grows across cities, every robotaxi benefits from the first one that encounters the hazard.

How operators can integrate by 2026

  • Software‑first uplift: Integrate DAI virtual sensors into the ADS safety supervisor and motion planner to enable weather‑aware speed, headway and routing.
  • Hardware optioning: Specify AIS on vehicles destined for rain‑prone corridors and motorway ruts; validate actuation timing within the worst‑case puddle profiles in local ODDs.
  • Connected operations: Connect fleets to ERC so detections propagate city‑wide, improving predictions for all vehicles, all day.

Europe has the regulations and partnerships to start robotaxi services in 2026. To scale safely through the continent’s rain, slush and ice, operators need predictive low‑grip intelligence and an active countermeasure to aquaplaning. Easyrain’s DAI, AIS and ERC provide precisely that — a practical safety stack designed for real‑world Europe.

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Virtual Sensors: Revolutionizing Automotive Safety and Efficiency

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In the evolving landscape of autonomous vehicles (AVs) and electric vehicles (EVs), predictive maintenance and road safety are becoming central pillars. Virtual sensors, particularly for tire wear monitoring, represent a technological leap forward compared to traditional physical sensors. They offer a transformative approach to vehicle data collection and analysis.

Key Differences: Virtual Sensors vs. Physical Sensors

Understanding the distinction between virtual and physical sensors is crucial for appreciating the advantages of this new paradigm:

Aspect Physical Sensors Virtual Sensors
Hardware Require dedicated components Utilize only existing in-vehicle sensors
Costs High: purchase, installation, maintenance Reduced: no additional hardware, OTA software updates
Maintenance Subject to failures, replacements, calibrations Remotely updateable, less prone to malfunctions
Flexibility Limited by physical placement Highly flexible, deployable wherever data is needed
Accuracy Depends on sensor quality and position Can match or exceed manual precision via AI models and data fusion
Scalability Limited by cost and infrastructure Very high due to software-only nature

Specific Advantages of Virtual Sensors

  • Elimination of Additional Hardware: Virtual sensors leverage data from existing vehicle sensors (e.g., ABS, accelerometers). This removes the need for extra physical sensors, reducing vehicle weight and complexity.
  • Reduced Total Costs: There are no costs for additional components, installation, or physical maintenance. Software updates can be distributed over-the-air (OTA), minimizing vehicle downtime.
  • Real-Time “On-the-Move” Monitoring: They enable continuous tire wear monitoring while the vehicle is in motion. Precision can be as high as 0.8 mm, comparable to manual laboratory measurements.
  • Enhanced Safety and Prevention: Virtual sensors promptly detect critical wear conditions. This prevents blowouts or skidding, significantly improving safety for AVs and EVs, where routine maintenance is less frequent.
  • Reliability and Validation: New virtual tire wear sensors have been extensively validated in real-world conditions. They demonstrate reliability across diverse vehicles, powertrains, and driving environments.
  • Ease of Update and Adaptability: Based on AI and machine learning algorithms, they can be continuously improved and adapted to new conditions or vehicle types without hardware interventions.
  • Accessibility and Scalability: Advanced monitoring becomes accessible to fleets and mass-market vehicles, not just premium models, fostering the future of mobility.

Virtual Sensor Technology in Autonomous Vehicles

The transformative role of virtual sensors in modern automotive safety.

Easyrain’s Strategic Role in Autonomous Driving Safety

Easyrain is at the forefront of this technological revolution, leveraging the power of virtual sensing to enhance road safety and vehicle performance, especially for AVs and EVs. Our suite of solutions directly aligns with the benefits offered by virtual sensors, addressing critical challenges in the automotive industry.

  • Comprehensive Virtual Sensor Suite: Easyrain offers a range of virtual sensors, including Virtual Sensor ITPMS, Virtual Sensor Aquaplaning, Virtual Sensor Ground, Virtual Sensor Wheel Misalignment, Virtual Sensor Snow & Ice, and Virtual Sensor Tire Wear. These systems detect real-time risk conditions often invisible to traditional sensors, enhancing the vehicle’s environmental perception.
  • DAI – Digital Advanced Information: Our powerful DAI platform integrates and interprets data from these virtual sensors. It provides predictive information to anticipate and prevent dangerous situations, a crucial aspect for autonomous navigation.
  • AIS – Aquaplaning Intelligent Solution: Easyrain’s patented AIS technology actively manages aquaplaning risk. This is a primary cause of loss of control on wet roads. AIS ensures active safety, complementing the predictive capabilities of virtual sensors.
  • Easyrain Cloud: The Easyrain Cloud infrastructure enables continuous data sharing and updates. This occurs between vehicles and road infrastructure, creating a connected ecosystem that enhances overall road safety and contributes to a smarter mobility future.

Easyrain Virtual Sensors in Action

Easyrain’s innovative solutions enhance safety through advanced virtual sensing.

Implications for the Automotive Future

  • Autonomous and Electric Vehicles: Virtual sensors are particularly strategic for AVs and EVs. In these vehicles, routine maintenance is less frequent, and safety relies heavily on continuous, predictive monitoring.
  • Sustainability: Less hardware translates to fewer materials, a reduced environmental footprint, and greater energy efficiency.
  • Continuous Innovation: The software-centric approach allows rapid response to new regulatory and market demands. It makes the vehicle increasingly “software-defined” [2].

Conclusion

Virtual sensors represent a revolution for the automotive industry. They offer tangible advantages in terms of cost, safety, reliability, and scalability compared to physical sensors. The recent introduction of advanced virtual tire wear sensors, validated on a large scale, demonstrates that this technology is ready to enhance the safety and efficiency of future mobility. As highlighted by market forecasts, the automotive sector in 2025 will increasingly feature smart and connected cars, driven by AI and IoT applications [1] [4]. Virtual sensors are at the core of this transformation [5].

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