The #InnovativeTsinghua team, led by Prof. Li Jun from School of Vehicle and Mobility, proposed a new #safetyevaluation method for #autonomousdriving systems based on the Safety of the Intended Functionality Index (#SOTIF)🚗 It helps reduce unknown risks and paves the way toward near-zero-accident autonomous driving!

he autonomous vehicle debate has shifted from "Can it work?" to "How fast can we scale?" Waymo proves the viability: their AI-powered fleet delivers 91% fewer serious crashes than human drivers across 20M+ autonomous miles. As they expand internationally (London 2026), we're seeing the infrastructure of tomorrow being built today. The real question: Is your industry ready for this level of AI-driven disruption? #FutureOfWork #AutonomousTech #AILeadership

As advanced as self-driving technology has become, the real world doesn’t always follow a script. A sudden pedestrian crossing the street, debris falling from a truck, an unexpected detour these random, real-life moments remind us that the road is full of variables no algorithm can fully anticipate. The challenge isn’t just teaching cars how to drive; it’s teaching them how to adapt. Building systems that can reason, predict, and make safe split-second decisions in chaos is where true innovation lies. Autonomous vehicles aren’t just about replacing human drivers. They’re about understanding and reacting to a world that’s beautifully unpredictable.

Autonomous driving technology is advancing rapidly, with companies testing AI-driven vehicles. 🚗⚙️ Safety, regulatory, and infrastructure challenges remain. Expect significant developments in the next few years! #Garisea #findyourride #smartcarbuying #car #ai #carlove #carsdaily #selfdrivingcar #tech #dreamcar #carlifestyle #carswithoutlimits

Precise path tracking remains one of the defining challenges of autonomous driving, balancing stability, accuracy, and adaptability across diverse road conditions. Researchers at the Korea National University of Transportation have proposed a hybrid control system that integrates the strengths of the Pure Pursuit and Stanley methods into a single, dynamic framework. Using a bicycle vehicle model and an Interactive Multiple Model (IMM) filter, the system probabilistically selects and blends both algorithms in real time, ensuring optimal control at any speed or curvature. Pure Pursuit offers smooth low-speed maneuvering, while Stanley enhances high-speed stability—the hybrid system dynamically merges both, cutting tracking errors and improving steering precision, particularly in sharp turns and variable curvature roads. Simulations confirm significant accuracy gains and reduced oscillation under complex driving scenarios. Designed for autonomous cars, trucks, and delivery robots, this approach advances the state of vehicle navigation, reducing energy use, maintenance, and safety risks. As computing efficiency improves, hybrid controllers like this could become foundational for next-generation autonomous mobility, providing seamless adaptability between urban precision and highway speed. #Schumpeter #EmergingTechnologies #AutonomousVehicles #MobilityTech #HybridControl #PathTracking #PurePursuit #StanleyMethod #VehicleDynamics #AIinMobility #SmartTransportation #AutomotiveEngineering #AdvancedAutomation #NavigationSystems #Robotics #AutonomousDriving #KoreaNationalUniversity #AIandMobility #IntelligentVehicles #TransportInnovation https://lnkd.in/dQQEfiM5

QKOIL™ transforms how autonomous vehicles recharge. Ideal for autonomous vehicle charging hubs, the QKOIL™ overhead wireless charging system automatically aligns and charges each vehicle, with no cables, plugs, or human intervention needed. The system’s overhead gantry positions a wireless charging coil above the vehicle, even when parked imperfectly, and retracts once charging is complete, ready for the next vehicle. This hands-free, fully automated solution enables efficient, continuous charging for robotaxis, delivery fleets, and self-driving shuttles. https://lnkd.in/gcF9W5R7 #WirelessCharging #AutonomousVehicles #EVCharging #FleetAutomation #SmartMobility

Sampling-Based Motion Planning in Autonomous Vehicles When navigating through complex environments filled with obstacles, dynamic objects, and road constraints, autonomous vehicles must plan safe and efficient paths in real-time. This is where Sampling-Based Motion Planners (SBMPs) like RRT (Rapidly-Exploring Random Tree) and RRT* become essential. These algorithms do not search the entire space mathematically; instead, they sample random points in the environment, incrementally building a tree or graph that explores feasible paths. Why They’re Powerful: - Efficiently handle high-dimensional search spaces - Easily adapt to non-linear vehicle kinematics - Can be optimized (like RRT*) for the shortest or smoothest paths - Robust for real-time decision-making in dynamic scenarios From parking assistance to autonomous highway merging, SBMPs are foundational to the motion planning layers in modern autonomous vehicle stacks. They not only find paths but also navigate the complexities of decision-making under uncertainty. #AutonomousVehicles #MotionPlanning #Robotics #RRT #RRTStar #PathPlanning #AIinMobility #SelfDrivingCars

Safe autonomous tech like Omar Autonomous can elevate and save 100 millions each year, and the path to scale is already here with real, affordable systems across every vehicle type. How are you thinking about bringing this to the world at pace? #AutonomousTech #RoadSafety #MobilityInnovation #ScaleTech

New research project CONTROL launched: More safety for autonomous driving on road and rail in complex environments How can autonomous cars, trucks, buses, and trains operate safely and reliably when traffic conditions constantly change due to unexpected obstacles or shifting weather? This is the central question of the research project CONTROL – short for Controlling Risk of Highly Automated Transportation Systems Operating in Complex Open Environments – which kicked off in October. As a project partner, dSPACE will work intensively over the next three years with other partners on the topic of Simulation Credibility. This framework forms a foundation for virtual homologation and provides a systematic approach to assessing the trustworthiness of models and simulations. The goal: to strengthen confidence in virtual testing methods and advance their use as a key technology for homologation. #AutonomousDriving #Road #Rail #SimulationCredibility #VirtualHomologation #SafetyInnovation #ResearchProject #CONTROL #dSPACE

The first elevators weren't trusted to run on their own. Even after push buttons were introduced, an operator sat inside, pressing the same buttons passengers could have pressed themselves. Why? Because trust doesn't change as fast as technology. The same is true for autonomous vehicles. A self-driving car is essentially a horizontal elevator. The tech may work, but adoption depends on how people feel about riding in it, and how society adapts to the shift. Dr. Sven Beiker unpacks why autonomy is more than a technical challenge, what fleets need to know before electrifying, and how people, not just systems, determine when new mobility becomes mainstream. Thanks to our sponsor Element Fleet Management!