Things to Do in L.A. Tap to enable a layout that focuses on the article. Chance of more showers in L.A., with a new storm set to hit Thursday A driver navigates a flooded street during a storm Monday in Santa Barbara. This is read by an automated voice. Please report any issues or inconsistencies here . Showers could linger in Los Angeles on Tuesday following four straight days of rain -- and even more rain is likely on Thursday and Friday.

In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection.

Alphabet's Waymo said on Wednesday that it would begin offering robotaxi rides that use freeways across San Francisco, Los Angeles and Phoenix, a first for the Google subsidiary as it steps up expansion amid global and domestic competition in the self-driving industry. Freeway rides will initially be available to early-access users, Waymo said. "When a freeway route is meaningfully faster, they can be matched with a freeway trip, providing quicker, smoother, and more efficient rides," it said. The race begins to make the world's best self-driving cars Waymo, which already operates in parts of the San Francisco Bay Area, is also extending operations to San Jose, including Mineta San Jose international airport, the second airport in its service area after Phoenix Sky Harbor. The move comes as Tesla expands its robotaxi service with safety monitors and drivers, and Zoox - backed by Amazon - offers free robotaxi rides on and around the Las Vegas Strip.

Waymo's Robotaxis Can Now Use the Highway, Speeding Up Longer Trips The Alphabet company's self-driving cars are opening up shop in more and more cities. When Google's self-driving car project began testing in the Bay Area back in 2009, its engineers focused on highways by sending its sensor-laden vehicles cruising down Interstate 280, which runs the length of Silicon Valley's peninsula. More than 15 years later, the cars are back on the freeway--this time without drivers. On Tuesday, the project, now an Alphabet subsidiary we all know as Waymo, announced that its robotaxi service would now drive on freeways in the San Francisco Bay Area, Los Angeles, and Phoenix. The new service marks another technical leap for Waymo, whose robotaxis currently serve five US metros: Atlanta, Austin, Los Angeles, Phoenix, and the San Francisco Bay Area.

In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection.

This paper presents crash rate benchmarks for evaluating US-based Automated Driving Systems (ADS) for multiple urban areas. The purpose of this study was to extend prior benchmarks focused only on surface streets to additionally capture freeway crash risk for future ADS safety performance assessments. Using publicly available police-reported crash and vehicle miles traveled (VMT) data, the methodology details the isolation of in-transport passenger vehicles, road type classification, and crash typology. Key findings revealed that freeway crash rates exhibit large geographic dependence variations with any-injury-reported crash rates being nearly 3.5 times higher in Atlanta (2.4 IPMM; the highest) when compared to Phoenix (0.7 IPMM; the lowest). The results show the critical need for location-specific benchmarks to avoid biased safety evaluations and provide insights into the vehicle miles traveled (VMT) required to achieve statistical significance for various safety impact levels. The distribution of crash types depended on the outcome severity level. Higher severity outcomes (e.g., fatal crashes) had a larger proportion of single-vehicle, vulnerable road users (VRU), and opposite-direction collisions compared to lower severity (police-reported) crashes. Given heterogeneity in crash types by severity, performance in low-severity scenarios may not be predictive of high-severity outcomes. These benchmarks are additionally used to quantify at the required mileage to show statistically significant deviations from human performance. This is the first paper to generate freeway-specific benchmarks for ADS evaluation and provides a foundational framework for future ADS benchmarking by evaluators and developers.

The core of the freeway merging control problem lies in dynamic queue propagation and dissipation linked to merging vehicle behavior. Traditionally, queuing is modeled through demand-supply interactions with time varying demand and fixed capacity. However, field observations show flow rates decrease during congestion at freeway merges due to the impact of intersecting traffic, a factor overlooked in fundamental diagrams. This manuscript introduces an analytical approach to characterize and control the dynamic multi-stage merging of autonomous vehicles, prioritizing traffic efficiency and safety. For the first time, the effective discharge rate at the merging point, reduced by the multi-stage dynamic merging process, is analytically derived using a closed form formulation. Leveraging this expression, performance metrics such as queue length and traffic delay are derived as the first objective. Additionally, a crash risk function is established to quantitatively assess potential collisions during the merging process, serving as the second objective. Finally, the problem is formulated as a dynamic programming model to jointly minimize delay and crash risk, with the merging location and speed as decision variables. Given the terminal state, the ramp vehicle merging task is formulated as a recursive optimization problem, employing backward induction to find the minimum cost solution. Numerical experiments using the NGSIM dataset validate the derived effective discharge rate. The results indicate that the proposed model outperforms two benchmark algorithms, leading to a more efficient and safer merging process.

As Los Angeles police struggled with another day of unrest in downtown L.A., several Waymo autonomous taxis were set on fire, sending black smoke billowing into the air. The dramatic images were captured during an afternoon of clashes between large groups who were protesting immigration raids by the Trump administration and L.A. police who were trying to maintain order. For some time, protesters blocked traffic on the 101 Freeway before California Highway Patrol officers slowly pushed them back. Police advised residents to avoid the the 101 Freeway through downtown L.A. Images of the Waymo cars on fire on Los Angeles Street were broadcast nationally as Los Angeles has become a flashpoint in the immigration debate. Tires were slashed, windows smashed, and anti-ICE messages spray-painted over the cars, which were parked in a row.

Anyone in Los Angeles will be able to digitally hail a Waymo robotaxi beginning Tuesday, but don't expect the driverless vehicle to jump onto a freeway. Waymo announced it will begin Waymo One service in Los Angeles County but will limit its vehicles to surface streets, where the startup owned by Google's parent, Alphabet, has been testing the sensor-laden cars for months with a limited group of passengers. The service can now be accessed by the public 24 hours a day on its app. Los Angeles officials have been skeptical, seeking more safety oversight of the vehicles as future growth looms and unions raise concerns about job losses. But California Gov. Gavin Newsom this year vetoed legislation that would have required driverless car companies to report more data on crashes.

I saw two visions of the future in Los Angeles last weekend. First, a Waymo Jaguar I-PACE pulled over to pick me up on a busy street in downtown L.A., spinning lidar sensors mounted on the hood like a second set of side mirrors. We inched comfortably through stop-and-go Saturday afternoon traffic and made an impressive left turn ahead of two lanes of oncoming cars as I said my prayers in the passenger seat. On the other hand, the robot lost its nerve trying to turn right across a crosswalk. As pedestrians cleared and the light turned from green to yellow to red, the Waymo remained fixed to the spot.