A widespread power failure plunged San Francisco into darkness on Saturday night, disrupting traffic citywide and forcing numerous self-driving Waymo taxis to stop abruptly in the middle of streets and intersections. As electricity went out across large portions of the city, traffic signals failed, leaving autonomous vehicles unable to operate as normal. Photos and videos shared by users on X showed Waymo robotaxis frozen in place, backing up traffic and creating hazardous conditions for other drivers. Waymo confirmed on Saturday evening that it had shut down its driverless ride-hailing service throughout San Francisco after footage circulated online showing its vehicles blocking roads during the blackout. "We have temporarily suspended our ride-hailing services in the San Francisco Bay Area due to the widespread power outage," Waymo spokesperson Suzanne Philion said in a statement to several news outlets.

LG TVs add'delete' option for Copilot A San Francisco power outage left Waymo's self-driving cars stranded at intersections Waymo halted its autonomous ride-hailing services in the city in response. Several of Waymo's autonomous vehicles were seen stuck in the middle of San Francisco streets following a significant power outage that took out the city's traffic lights. Waymo responded to the power outage by suspending its ride-hailing services in the city, but images and videos on social media showed the self-driving taxis stopped at intersections with hazard lights on. We have temporarily suspended our ride-hailing services in the San Francisco Bay Area due to the widespread power outage, Suzanne Philion, a spokesperson for Waymo, told Engadget in an email. Our teams are working diligently and in close coordination with city officials, and we are hopeful to bring our services back online soon.

Automaker Zeekr and autonomous driving technology company Waymo have joined forces to create a groundbreaking autonomous vehicle designed specifically for ride-hailing services. The result of this collaboration is the Zeekr RT, the world's first mass-produced, purpose-built autonomous vehicle, which is now ready for delivery to Waymo for robotaxi testing. This partnership combines Zeekr's expertise in electric vehicle manufacturing with Waymo's advanced self-driving technology. GET SECURITY ALERTS & EXPERT TECH TIPS -- SIGN UP FOR KURT'S THE CYBERGUY REPORT NOW The Zeekr RT is equipped with an impressive array of 13 cameras, four lidar units, six radar sensors and external audio receivers, ensuring a 360-degree view of its surroundings. To maintain optimal performance in various weather conditions, the Zeekr RT features a specially designed system to keep its sensors clean.

On-demand ride-hailing services like DiDi, Uber, and Lyft have transformed urban transportation, offering unmatched convenience and flexibility. In this paper, we introduce DiMA, an LLM-powered ride-hailing assistant deployed in DiDi Chuxing. Its goal is to provide seamless ride-hailing services and beyond through a natural and efficient conversational interface under dynamic and complex spatiotemporal urban contexts. To achieve this, we propose a spatiotemporal-aware order planning module that leverages external tools for precise spatiotemporal reasoning and progressive order planning. Additionally, we develop a cost-effective dialogue system that integrates multi-type dialog repliers with cost-aware LLM configurations to handle diverse conversation goals and trade-off response quality and latency. Furthermore, we introduce a continual fine-tuning scheme that utilizes real-world interactions and simulated dialogues to align the assistant's behavior with human preferred decision-making processes. Since its deployment in the DiDi application, DiMA has demonstrated exceptional performance, achieving 93% accuracy in order planning and 92% in response generation during real-world interactions. Offline experiments further validate DiMA capabilities, showing improvements of up to 70.23% in order planning and 321.27% in response generation compared to three state-of-the-art agent frameworks, while reducing latency by $0.72\times$ to $5.47\times$. These results establish DiMA as an effective, efficient, and intelligent mobile assistant for ride-hailing services.

The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services. A key issue in this context is the vehicle rebalancing problem, where idle vehicles are moved to areas with anticipated demand. Without equitable approaches in demand forecasting and rebalancing strategies, these practices can further deepen existing inequities. In the realm of ride-hailing, three main facets of fairness are recognized: algorithmic fairness, fairness to drivers, and fairness to riders. This paper focuses on enhancing both algorithmic and rider fairness through a novel vehicle rebalancing method. We introduce an approach that combines a Socio-Aware Spatial-Temporal Graph Convolutional Network (SA-STGCN) for refined demand prediction and a fairness-integrated Matching-Integrated Vehicle Rebalancing (MIVR) model for subsequent vehicle rebalancing. Our methodology is designed to reduce prediction discrepancies and ensure equitable service provision across diverse regions. The effectiveness of our system is evaluated using simulations based on real-world ride-hailing data. The results suggest that our proposed method enhances both accuracy and fairness in forecasting ride-hailing demand, ultimately resulting in more equitable vehicle rebalancing in subsequent operations. Specifically, the algorithm developed in this study effectively reduces the standard deviation and average customer wait times by 6.48% and 0.49%, respectively. This achievement signifies a beneficial outcome for ride-hailing platforms, striking a balance between operational efficiency and fairness.

As ride-hailing services have experienced significant growth, the majority of research has concentrated on the dispatching mode, where drivers must adhere to the platform's assigned routes. However, the broadcasting mode, in which drivers can freely choose their preferred orders from those broadcast by the platform, has received less attention. One important but challenging task in such a system is the determination of the optimal matching radius, which usually varies across space, time, and real-time supply/demand characteristics. This study develops a Transformer-Encoder-Based (TEB) model that predicts key system performance metrics for a range of matching radii, which enables the ride-hailing platform to select an optimal matching radius that maximizes overall system performance according to real-time supply and demand information. To simultaneously maximize multiple system performance metrics for matching radius determination, we devise a novel multi-task learning algorithm that enhances convergence speed of each task (corresponding to the optimization of one metric) and delivers more accurate overall predictions. We evaluate our methods in a simulation environment specifically designed for broadcasting-mode-based ride-hailing service. Our findings reveal that dynamically adjusting matching radii based on our proposed predict-then-optimize approach significantly improves system performance, e.g., increasing platform revenue by 7.55% and enhancing order fulfillment rate by 13% compared to benchmark algorithms.

GM, Cruise and Honda are teaming up to introduce a driverless ride-hailing service in Japan, which could launch in early 2026 if things go according to plan. The companies have entered a memorandum of understanding to form a joint venture for the project, and they're hoping to establish the company in the first half of 2024, provided they're able to secure the necessary regulatory approvals by then. Their ride-hailing service will deploy the Cruise Origin electric shuttle van that the companies had developed together. It's a self-driving vehicle with no steering wheel or even a driver's seat, which means it also has no pedals and no rearview mirror. Instead, it has a big cabin space where up to six passengers can sit facing each other, and its doors slide open like a subway's. "The opportunity for the ridehail service in Japan, which is expected to be the first of its kind, is huge," GM said in its announcement.

Waymo only just reached Los Angeles earlier this year, but that isn't stopping it from expanding further. The company is expanding its Waymo One ride-hailing service to Austin. The first phase starts this fall, with completely driverless operations and public rides coming in the months ahead. The coverage will be "truly useful," Waymo claims -- it should cover major stretches of the Texas capital, including the downtown core as well as well-known areas like Barton Hills and Hyde Park. You can join a waitlist today.

Online ride-hailing services have become a prevalent transportation system across the world. In this paper, we study a challenging problem of how to direct vacant taxis around a city such that supplies and demands can be balanced in online ride-hailing services. We design a new reward scheme that considers multiple performance metrics of online ride-hailing services. We also propose a novel deep reinforcement learning method named Deep-Q-Network with Action Mask (AM-DQN) masking off unnecessary actions in various locations such that agents can learn much faster and more efficiently. We conduct extensive experiments using a city-scale dataset from Chicago. Several popular heuristic and learning methods are also implemented as baselines for comparison. The results of the experiments show that the AM-DQN attains the best performances of all methods with respect to average failure rate, average waiting time for customers, and average idle search time for vacant taxis.

As ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning Convolutional Neural Network for short-term prediction of demand for ride-hailing services. UberNet empploys a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. The proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UberNet, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. Further, Ubernet's prediction performance is better when using economic, social and built environment features. This suggests that Ubernet is more naturally suited to including complex motivators in making real-time passenger demand predictions for ride-hailing services.