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🚧 Construction zone ahead? No problem. WeRide Driving (WRD 3.0) handles roadworks with ease, merging smoothly, adapting to lane changes, and reading traffic signals in real-time. Watch how WeRide secured its 5th win below. $WRD

WeRide.ai

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483,218 views • 4 months ago •via X (Twitter)

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Guangzhou's autonomous driving sector is in the global spotlight. #Nvidia's latest disclosure shows it owns 1.74 million WeRide shares, valued at $24.65 million. Nvidia's bet on WeRide is strategic. Autonomous driving's real-time data processing relies heavily on GPUs, and Nvidia aims to transition from a chip supplier to an autonomous driving ecosystem architect. This move is a mutual attraction. Prior to its 2024 Nasdaq listing, #WeRide raised over $1 billion in six funding rounds, with investors including Yutong Bus, GAC Group, Bosch, SenseTime, and He Xiaopeng. What makes WeRide so attractive? #Guangzhou

Guangzhou's autonomous driving sector is in the global spotlight. #Nvidia's latest disclosure shows it owns 1.74 million WeRide shares, valued at $24.65 million. Nvidia's bet on WeRide is strategic. Autonomous driving's real-time data processing relies heavily on GPUs, and Nvidia aims to transition from a chip supplier to an autonomous driving ecosystem architect. This move is a mutual attraction. Prior to its 2024 Nasdaq listing, #WeRide raised over $1 billion in six funding rounds, with investors including Yutong Bus, GAC Group, Bosch, SenseTime, and He Xiaopeng. What makes WeRide so attractive? #Guangzhou

Glocal

219,264 views • 1 year ago

New lane? No problem. Our universal autonomy platform adapts in real time to all kinds of dynamic road environments. Watch our AI driver detect this construction zone, interpret the cones as temporary lane markings, and proceed with the safest path forward. #DrivenByNuro #SelfDriving #AutonomousVehicles

New lane? No problem. Our universal autonomy platform adapts in real time to all kinds of dynamic road environments. Watch our AI driver detect this construction zone, interpret the cones as temporary lane markings, and proceed with the safest path forward. #DrivenByNuro #SelfDriving #AutonomousVehicles

Nuro

13,685 views • 6 months ago

A smooth overtake is about more than passing. All-new XPENG VLA 2.0 anticipates the bus, e-bike, and oncoming traffic—adjusting in real time to overtake decisively and return to lane with ease. Driving should always feel this smooth. $XPEV

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XPENG

467,251 views • 1 month ago

Just took my Tesla Cybertruck out with FSD Supervised v13.2.9, and it’s next-level! 🤖 The end-to-end driving network is so smooth – nailed lane changes and handled a tight two-lane road like a pro. The 36 Hz AI4 video inputs and low latency make it feel like the truck’s reading the road in real-time.

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Tesla Owners Silicon Valley

66,852 views • 1 year ago

We had an unbelievable demo at CES. Wayve drove autonomously in Las Vegas zero-shot — no prior training on the city, no disengagements all week. We brought three vehicles from our Silicon Valley fleet to Vegas. Despite driving in 500 cities last year, our AI had never seen Las Vegas before. Guests chose their own routes across urban streets and highways, and the system performed flawlessly. I was there for 24 hours and saw: * Confident lane changes to overtake slower traffic * Correct handling of a blinking yellow left turn (not used in California) * Robust navigation through frequent roadworks * Seamless compliance with traffic marshals' hand signals directing vehicles leaving the convention center — my favourite moment in the video below! More evidence our AI can drive any vehicle, anywhere.

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Alex Kendall

80,036 views • 9 months ago

🚨 Many of you asked us to test highway driving with Tesla Full Self-Driving v14.2.1.25. Here’s what we noticed: ✅ Speed Profiles are significantly improved. Hurry Mode is no longer capped at 10 MPH over the speed limit, and now travels with the flow of traffic. This is much more comfortable and requires no assistance on the driver’s end. Previously, I was helping it switch lanes with v14.2.1. ✅ Lane changes are performed much more confidently and FSD seems to make proper maneuvers at proper times. This was a regression in v14.2.1. This version is significantly better. ✅ Merging, switching lanes, and passing are all performed with zero interventions. Perhaps the most natural feeling of highway driving in an FSD update in some time. Well done Tesla AI

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TESLARATI

73,811 views • 10 months ago

‼️ MARCH 2026 WEB3 PANEL: RIPPLE IS MAKING HUGE PROGRESS BUILDING OUT ITS GLOBAL NETWORK WITH XRP ‼️ In a recent Crypto Valley panel in Zurich, Ripples Tania Griffith shared how the company is driving real progress with XRP and expanding its global payments network. She broke down the latest successes in crypto adoption, explaining how banks are becoming far more comfortable with blockchain technology and how today’s crypto liquidity is finally strong enough to support true 24/7 payments. Watch her full breakdown below 👇

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SMQKE

34,597 views • 6 months ago

Imagine driving on a National Highway and the bus ahead of you suddenly stops - sideways, blocking the lane. Then the driver gets out and starts hitting your car window. This is what happened near Acharapakkam, Tamil Nadu - caught on dashcam. No crash. But every element for one was present: a stopped vehicle mid-highway, an aggressive confrontation, and traffic having to react in real time. What do you do if this happens to you - stay in the car, or step out? 🚌⚠️

Imagine driving on a National Highway and the bus ahead of you suddenly stops - sideways, blocking the lane. Then the driver gets out and starts hitting your car window. This is what happened near Acharapakkam, Tamil Nadu - caught on dashcam. No crash. But every element for one was present: a stopped vehicle mid-highway, an aggressive confrontation, and traffic having to react in real time. What do you do if this happens to you - stay in the car, or step out? 🚌⚠️

Girl-on-the-Roadz

22,585 views • 2 months ago

New footage from Kraków shows Tesla FSD (Supervised) tackling the "final boss" of driving: Polish roadworks. >One of the most impressive feats is how the AI correctly prioritizes temporary yellow lines over permanent white ones. Even when the original markings are confusing, faded, or overlapping, the neural network maintains the correct path. >The system instantly identifies traffic cones, bollards, and barriers. Instead of just stopping, it creates a dynamic "buffer zone" to navigate safely through narrow gaps. >The car doesn't just follow lines blindly; it observes the flow of other vehicles and responds to sudden changes in the road layout. If other car blocks the path, the system evaluates the space and decides when to bypass it based on real-time traffic behavior. Sawyer Merritt Szymon Janus TL;DR: The system is way smarter than I gave it credit for.

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Michał Podlewski

239,225 views • 6 months ago

FSD 14.2 STUNS IN WINTER DRIVING! Just got done driving ~100 miles on a snowy interstate and a little suburban driving. A few notes: FSD absolutely, definitively detects snow as a separate substance from rain. As the snow began to fall, there came a point where FSD first detected the snow and immediately slowed. From then on, it drove in a completely different profile. Generally, V14.2 kept 40-55mph on a 70mph interstate. When driving in Sloth/Chill and you try to increase the speed profile to Standard/Hurry, the truck displays a red error saying inclement weather is preventing FSD from speeding up. Fantastic! V14.2 takes corners slow as molasses to keep traction. It does NOT shoot gaps and pull out in front of other vehicles. V14.2 waits for BIG gaps to make its merge into traffic. It is properly cautious! One instance of Red Hands occurred when it was merging into a center left turn lane. This center lane was the only lane not plowed, so either 1. it got confused with lane markings or 2. Slid sideways very slightly and got upset. On straightaways on the interstate in areas with little snow coverage, V14.2 would speed up close to the speed limit. While the roads were wet today from road treatment, I was concerned for slick & frozen areas and manually backed off on the speed. While not technically unsafe, I did this out of an abundance of caution. IMO this is a grey area between "driving safely" and "not driving how I do" lol Not much ice today. Just snow and slush. Tonight, 6-12 inches of fresh snow is forecasted to fall here. I'll be out at dawn tomorrow to test in worse conditions with less traffic. Stay tuned.

The Cybertruck Guy

180,271 views • 10 months ago

Autonomous driving through extremely-tight-dynamic environments with complex, stochastic, and adversarial traffic-dynamics, or simply through an absolute chaos, on sub-urban unstructured roads in India. This kind of traffic and environment has never been attempted in the history of #autonomousdriving. There were no traffic-rules to abide by on this road, other than to perform a left-sided avoidance, if the other obstacles follow the same, else the vehicle will have to change its plan in a stochastic manner, in several of the adversarial multi-agent negotiation settings encountered throughout the autonomous navigation. This demos tested our motion planning and decision making framework to its limits, showcasing its robustness in negotiating such traffic-dynamics with ease. This demo was done on mostly a very narrow road, suited mostly for one-way navigation, but as is customary in India, bidirectional traffic is active on such narrow roads. It can be seen throughout navigation that the incoming vehicles didn't allow any gaps for our #AutonomousVehicles, forcing it to negotiate passively-aggressively its own path through the chaos. Furthermore, obstacles overtaking us didn't follow any rules either, and zig-zagged and moved in crisscross fashion, challenging our motion and behaviour planning software, which negotiated all such scenarios with ease. There were only two points where our vehicle came to halt, when two girls on a two-wheeler didn't stop and just kept on navigating, despite our vehicle being closer to the narrow passage and it having the right of way, and despite a bike being parked over there by someone, making it a very challenging scenario both for the humans and for the decision making autonomous agent(s). This demo was done in the Awadhpuri area, on the Durga Mata road. This framework was last shown in relatively much sparser traffic in our Kankali Kali Mata demo last month. It is being scaled further with deep unsupervised and #reinforcementlearning , and in the coming weeks, it will play a critical role in our endeavour to solving the Level-4 autonomy problem by the end of the year. This kind of traffic negotiation has never been attempted by any autonomous driving company ever. While a 90-degree turn is usually discussed as a corner case in the West, our autonomous vehicle negotiated a blind 90-degree corner, with traffic, with ease. #deeplearning #MachineLearning Swaayatt Robots

Autonomous driving through extremely-tight-dynamic environments with complex, stochastic, and adversarial traffic-dynamics, or simply through an absolute chaos, on sub-urban unstructured roads in India. This kind of traffic and environment has never been attempted in the history of #autonomousdriving. There were no traffic-rules to abide by on this road, other than to perform a left-sided avoidance, if the other obstacles follow the same, else the vehicle will have to change its plan in a stochastic manner, in several of the adversarial multi-agent negotiation settings encountered throughout the autonomous navigation. This demos tested our motion planning and decision making framework to its limits, showcasing its robustness in negotiating such traffic-dynamics with ease. This demo was done on mostly a very narrow road, suited mostly for one-way navigation, but as is customary in India, bidirectional traffic is active on such narrow roads. It can be seen throughout navigation that the incoming vehicles didn't allow any gaps for our #AutonomousVehicles, forcing it to negotiate passively-aggressively its own path through the chaos. Furthermore, obstacles overtaking us didn't follow any rules either, and zig-zagged and moved in crisscross fashion, challenging our motion and behaviour planning software, which negotiated all such scenarios with ease. There were only two points where our vehicle came to halt, when two girls on a two-wheeler didn't stop and just kept on navigating, despite our vehicle being closer to the narrow passage and it having the right of way, and despite a bike being parked over there by someone, making it a very challenging scenario both for the humans and for the decision making autonomous agent(s). This demo was done in the Awadhpuri area, on the Durga Mata road. This framework was last shown in relatively much sparser traffic in our Kankali Kali Mata demo last month. It is being scaled further with deep unsupervised and #reinforcementlearning , and in the coming weeks, it will play a critical role in our endeavour to solving the Level-4 autonomy problem by the end of the year. This kind of traffic negotiation has never been attempted by any autonomous driving company ever. While a 90-degree turn is usually discussed as a corner case in the West, our autonomous vehicle negotiated a blind 90-degree corner, with traffic, with ease. #deeplearning #MachineLearning Swaayatt Robots

Sanjeev Sharma

125,363 views • 2 years ago

🚀 Introducing Atlas Miner: Autonomous DePIN Driving Networks. 🔹 1,200 pre-orders: We are overwhelmed by the demand for Atlas NAVI Miners. 1,200 drivers joined the whitelist and the first 100 can buy it NOW, with more to come each week as we increase our capacities progressively. Order your Atlas Miner here: 🔹 First of its kind: Custom-built by Atlas Navi, Atlas Miner is a hardware device seamlessly integrated into our decentralized physical infrastructure network (DePIN) communicating with other Atlas Navi drivers in real time. 🔹 Connects to any dash camera: Plug & play with almost any aftermarket dash camera – even inexpensive ones (Note: Doesn't work with factory-installed cameras yet.) 🔹 AI-powered real-time analysis: Using the latest computer vision machine learning algorithms, Atlas Miner detects traffic incidents, roadblocks, lane closures, heavy traffic, police vehicles, and more. Alerts drivers in real-time via the Atlas Navi ecosystem. 🔹 Benefits for miners (Drivers using the Atlas Miner): Generate rewards for contributing data. Other drivers get vital traffic info without using an app – just plug it in and go. 🔹 For fleet managers: Reliable income and essential driver monitoring. Access GPS data, trip history, and driving behavior through our upcoming Fleet Management Dashboard. 🔹 For smart cities: Anonymized and aggregated data becomes invaluable, creating revenue opportunities with $NAVI tokens through the upcoming Traffic Dashboard. Join us in transforming the driving experience and paving the way for smarter cities! 🌍🚗💡 #AtlasMiner #DePIN #NAVI #AI #SmartCities #FleetManagement #TrafficTech

🚀 Introducing Atlas Miner: Autonomous DePIN Driving Networks. 🔹 1,200 pre-orders: We are overwhelmed by the demand for Atlas NAVI Miners. 1,200 drivers joined the whitelist and the first 100 can buy it NOW, with more to come each week as we increase our capacities progressively. Order your Atlas Miner here: 🔹 First of its kind: Custom-built by Atlas Navi, Atlas Miner is a hardware device seamlessly integrated into our decentralized physical infrastructure network (DePIN) communicating with other Atlas Navi drivers in real time. 🔹 Connects to any dash camera: Plug & play with almost any aftermarket dash camera – even inexpensive ones (Note: Doesn't work with factory-installed cameras yet.) 🔹 AI-powered real-time analysis: Using the latest computer vision machine learning algorithms, Atlas Miner detects traffic incidents, roadblocks, lane closures, heavy traffic, police vehicles, and more. Alerts drivers in real-time via the Atlas Navi ecosystem. 🔹 Benefits for miners (Drivers using the Atlas Miner): Generate rewards for contributing data. Other drivers get vital traffic info without using an app – just plug it in and go. 🔹 For fleet managers: Reliable income and essential driver monitoring. Access GPS data, trip history, and driving behavior through our upcoming Fleet Management Dashboard. 🔹 For smart cities: Anonymized and aggregated data becomes invaluable, creating revenue opportunities with $NAVI tokens through the upcoming Traffic Dashboard. Join us in transforming the driving experience and paving the way for smarter cities! 🌍🚗💡 #AtlasMiner #DePIN #NAVI #AI #SmartCities #FleetManagement #TrafficTech

ATLAS NAVI | AI Navigation APP with 1M Downloads

359,404 views • 1 year ago

I'm playing GTA V on a PS3 and its been such a fun experience. I remember when was a kid and playing this for the first time, I didn't give a damn about how BAD the framerate was during driving. I was constantly in awe by how realistic this game looked back then that the framerate wasn't something that was going to stop me from enjoying it. Don't get me wrong, this game is running on a PS3 that has 256mb of RAM and 256mb of VRAM which is some black magic itself and the fact that Rockstar Games managed to have such a massive open world game with that much traffic and NPCs running in real-time is a feat on its own.

I'm playing GTA V on a PS3 and its been such a fun experience. I remember when was a kid and playing this for the first time, I didn't give a damn about how BAD the framerate was during driving. I was constantly in awe by how realistic this game looked back then that the framerate wasn't something that was going to stop me from enjoying it. Don't get me wrong, this game is running on a PS3 that has 256mb of RAM and 256mb of VRAM which is some black magic itself and the fact that Rockstar Games managed to have such a massive open world game with that much traffic and NPCs running in real-time is a feat on its own.

NikTek

253,924 views • 5 months ago

Autonomous driving through very dense dynamic traffic, with extremely tight-complex-stochastic traffic-dynamics on sub-urban roads, connecting to an open ground, with absolutely zero traffic-rules. This is the most heavily cluttered environment where we have tested our #autonomousdriving technology, presenting many of the adversarial negotiation scenarios as well, throughout the autonomous navigation task. This demo was done at the Mata Baglamukhi Madir campus in the city of Nalkheda, in MP, India, and was done in the presence of heavy police forces deployed that day on the ground, as can be seen in our demo. Our autonomous vehicle starts from the temple with a generic open environment, with zero traffic rules, with very narrow corridors created out of barricades for vehicles movement by the security forces. In the corridor no two vehicles can pass through at the same time, and our vehicle was tasked with driving through this corridor, while negotiating its way from any traffic, two-wheelers, or pedestrians it faces, with dense presence of bikes and cars on either side, presenting a very challenging environment for #autonomousvehicles. The vehicle exits the open area, and then assumes generic dual lane navigation, avoiding both static and dynamic obstacles, before encountering a police check-post, where the vehicle is supposed to wait if the barricade is closed, and proceed if open. Upon exiting the checkpost, the vehicle negotiates a traffic-intersection with stochastic and adversarial driving behaviour of other vehicles on the road. Our vehicle continuously faced heavily cluttered traffic scene, where entities on the road can execute a random driving pattern, making the decision making task very challenging. We did the demo over a period of two days, successfully executing multiple (30+) trials in this setting. This demo was again a culmination of our prior works and demos: Kankali Kali Mata demo, on-roads, bidirectional negotiation capability on single lane roads, and open environment Level-5 negotiation capability as showcased in our Toll-Plaza demo. We again scaled up classical decision making and motion planning algorithmic framework, to adapt to such a level of density of obstacles on the road. This framework is further being scaled up with #reinforcementlearning and unsupervised #deeplearning at Swaayatt Robots. We will again do a demo in the month of June here, showcasing autonomously acquired skills to pave the way for Level-5 autonomous driving, and to solve the Level-4 autonomy problem by the end of 2024. #MachineLearning

Autonomous driving through very dense dynamic traffic, with extremely tight-complex-stochastic traffic-dynamics on sub-urban roads, connecting to an open ground, with absolutely zero traffic-rules. This is the most heavily cluttered environment where we have tested our #autonomousdriving technology, presenting many of the adversarial negotiation scenarios as well, throughout the autonomous navigation task. This demo was done at the Mata Baglamukhi Madir campus in the city of Nalkheda, in MP, India, and was done in the presence of heavy police forces deployed that day on the ground, as can be seen in our demo. Our autonomous vehicle starts from the temple with a generic open environment, with zero traffic rules, with very narrow corridors created out of barricades for vehicles movement by the security forces. In the corridor no two vehicles can pass through at the same time, and our vehicle was tasked with driving through this corridor, while negotiating its way from any traffic, two-wheelers, or pedestrians it faces, with dense presence of bikes and cars on either side, presenting a very challenging environment for #autonomousvehicles. The vehicle exits the open area, and then assumes generic dual lane navigation, avoiding both static and dynamic obstacles, before encountering a police check-post, where the vehicle is supposed to wait if the barricade is closed, and proceed if open. Upon exiting the checkpost, the vehicle negotiates a traffic-intersection with stochastic and adversarial driving behaviour of other vehicles on the road. Our vehicle continuously faced heavily cluttered traffic scene, where entities on the road can execute a random driving pattern, making the decision making task very challenging. We did the demo over a period of two days, successfully executing multiple (30+) trials in this setting. This demo was again a culmination of our prior works and demos: Kankali Kali Mata demo, on-roads, bidirectional negotiation capability on single lane roads, and open environment Level-5 negotiation capability as showcased in our Toll-Plaza demo. We again scaled up classical decision making and motion planning algorithmic framework, to adapt to such a level of density of obstacles on the road. This framework is further being scaled up with #reinforcementlearning and unsupervised #deeplearning at Swaayatt Robots. We will again do a demo in the month of June here, showcasing autonomously acquired skills to pave the way for Level-5 autonomous driving, and to solve the Level-4 autonomy problem by the end of 2024. #MachineLearning

Sanjeev Sharma

332,976 views • 2 years ago