We present The Matrix, a foundational realistic world simulator capable of generating infinitely long 720p high-fidelity real-scene video streams with real-time, responsive control in both first-and third-person perspectives. Trained on limited supervised data from video games like Forza Horizon 5 and Cyberpunk 2077, complemented by large-scale unsupervised footage from real-world settings like Tokyo streets, The Matrix allows users to traverse diverse terrains--deserts, grasslands, water bodies, and urban landscapes--in continuous, uncut hour-long sequences. With speeds of up to 16 FPS, the system supports real-time interactivity and demonstrates zero-shot generalization, translating virtual game environments to real-world contexts where collecting continuous movement data is often infeasible. For example, The Matrix can simulate a BMW X3 driving through an office setting--an environment present in neither gaming data nor real-world sources. This approach showcases the potential of game data to advance robust world models, bridging the gap between simulations and real-world applications in scenarios with limited data.

Chinese Drivers Are Using Tiny Plastic Heads to Fool Tesla's Autopilot Safeguards A cottage industry of celebrity figurines, blinking screens, and other DIY gadgets is helping drivers bypass Tesla's distracted-driving controls. In China, for just $30, you can have Dwayne Johnson drive your Tesla for you. Sounds too cheap to be true? What you're actually buying is a tiny replica of The Rock's head, designed to sit above the rearview mirror and trick Tesla into thinking an attentive driver is behind the wheel. Tesla's self-driving system appears unable to tell the difference between the figurines and a real person, allowing the actual driver to look away from the road, scroll through their phone, or even doze off--activities that are supposed to be prohibited while assisted-driving features are engaged.

Accurately modeling pedestrian intention and understanding driver decision-making processes are critical for the development of safe and socially aware autonomous driving systems. However, existing datasets primarily emphasize observable behavior, offering limited insight into the underlying causal reasoning that informs human interpretation and response during traffic interactions. To address this gap, we introduce PSI, a benchmark dataset that captures the dynamic evolution of pedestrian crossing intentions from the driver's perspective, enriched with human-annotated textual explanations that reflect the reasoning behind intention estimation and driving decision making. These annotations offer a unique foundation for developing and benchmarking models that combine predictive performance with interpretable and human-aligned reasoning. PSI supports standardized tasks and evaluation protocols across multiple dimensions, including pedestrian intention prediction, driver decision modeling, reasoning generation, and trajectory forecasting and more. By enabling causal and interpretable evaluation, PSI advances research toward autonomous systems that can reason, act, and explain in alignment with human cognitive processes.

Waymo's monthly membership seems like a bad deal Waymo's monthly membership seems like a bad deal You'll pay way mo' for a lot less compared to the competition. Waymo -- the Alphabet-owned driverless taxi service which has seen a rapid expansion in recent years -- is rolling out a new rewards program today. The service is called Waymo Premier, and it promises priority pickups along with a 10 percent in-app rebate applied to future rides. Subscribers will also get fee-free cancellations, though only up to five a month. Lastly, Premier gives subscribers the chance to be among the first to use Waymo in new cities as the service expands, which is certainly one way to reframe the concept of paying to beta test those new coverage areas.

Unsupervised and open-vocabulary 3D object detection has recently gained attention, particularly in autonomous driving, where reducing annotation costs and recognizing unseen objects are critical for both safety and scalability. However, most existing approaches uniformly annotate 3D bounding boxes, ignore objects' physical states, and require multiple self-training iterations for annotation refinement, resulting in suboptimal quality and substantial computational overhead. To address these challenges, we propose OpenBox, a two-stage automatic annotation pipeline that leverages a 2D vision foundation model. In the first stage, OpenBox associates instance-level cues from 2D images processed by a vision foundation model with the corresponding 3D point clouds via context-aware refinement. In the second stage, it categorizes instances by rigidity and motion state, then generates adaptive bounding boxes with class-specific size statistics. As a result, OpenBox produces high-quality 3D bounding box annotations without requiring self-training. Experiments on the Waymo Open Dataset (WOD), the Lyft Level 5 Perception dataset, and the nuScenes dataset demonstrate improved accuracy and efficiency over baselines.

It might not feel all that different from older World Cups--for better or worse. Waymo, the Alphabet subsidiary offering robotaxi rides in 11 US metros right now, says it's ready for the FIFA World Cup . Match attendees can catch driverless rides to six of the 16 North American venues: stadiums in Atlanta, Houston, Los Angeles, Miami, and the San Francisco Bay Area. The sprawling football event, expected to attract some 6.5 million visitors to the continent over more than a month, could prove an exciting close-up for Waymo . The company says it's serving half-a-million paid rides a week--paltry stuff compared to the likes of ride-hail giants Uber and Lyft, but more impressive once you remember that the things don't have drivers.

We introduce BO4Mob, a new benchmark framework for high-dimensional Bayesian Optimization (BO), driven by the challenge of origin-destination (OD) travel demand estimation in large urban road networks. Estimating OD travel demand from limited traffic sensor data is a difficult inverse optimization problem, particularly in real-world, large-scale transportation networks. This problem involves optimizing over high-dimensional continuous spaces where each objective evaluation is computationally expensive, stochastic, and non-differentiable. BO4Mob comprises five scenarios based on real-world San Jose, CA road networks, with input dimensions scaling up to 10,100. These scenarios utilize high-resolution, open-source traffic simulations that incorporate realistic nonlinear and stochastic dynamics. We demonstrate the benchmark's utility by evaluating five optimization methods: three state-of-the-art BO algorithms and two non-BO baselines. This benchmark is designed to support both the development of scalable optimization algorithms and their application for the design of data-driven urban mobility models, including high-resolution digital twins of metropolitan road networks.

A Waymo nearly hit me, but I'm still optimistic about driverless cars A near miss with a Waymo while cycling through London hasn't changed my optimistic stance on driverless cars, but we can't ever let our guard down, says Matthew Sparkes Waymo driverless cars are in London, but is this a positive move for road safety? Waymo's driverless cars have been rolling through London for months, although they aren't taking passengers yet and a human sits ready to seize control if needed. Every time I've encountered them, they have seemed cautious and predictable. But recently, I had a near miss. I was circling a roundabout as I cycled home from work and a Waymo was about to pull onto it in front of me.

The automaker today is turning on vehicle-to-grid charging for its GM Energy customers. Will people actually use it? Some 250,000 electric vehicles manufactured by General Motors are driving around the US today--right now!--with an oft-secret capability: Their big, powerful batteries can charge other things. Potentially appliances, homes, and now, thanks to a software update pushed by the automaker this week, an electrical grid . Twelve of GM's EVs have this "bidirectional charging" capability, way more than US competitors' battery-electrics.

With a competitive price, winning design, and better performance than the R1, Rivian could be set to break into the big leagues. Just make sure you get the right model with the right tech. For years, Rivian made one argument: Serious all-electric adventure vehicles had to cost serious money. The R1S, still one of the more capable off-roaders on any surface, starts at $75,900. The R2 is Rivian's answer to its own problem.