You can now get rides from fully driverless Waymo cars in Nashville through the Lyft app. It's the first market where Waymo vehicles are available across both Lyft and the autonomous vehicle company's own application. You can get matched with a Waymo app when you request any Standard, Priority Pickup, Wait & Save or Extra Comfort ride. For now, though, your route must start and end within Waymo's service area in central Nashville to be eligible. Waymo's service area on Lyft covers Downtown/Broadway, North Nashville/Germantown, East Nashville, Midtown and South Nashville.

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.

The paper addresses the Vehicle Relocation Problem in free-floating car-sharing services by presenting a solution focused on strategies for repositioning vehicles and transferring personnel with the use of scooters. Our method begins by dividing the service area into zones that group regions with similar temporal patterns of vehicle presence and service demand, allowing the application of discrete optimization methods. In the next stage, we propose a fast ranking-based algorithm that makes its decisions on the basis of the number of cars available in each zone, the projected probability density of demand, and estimated trip durations. The experiments were carried out on the basis of real-world data originating from a major car-sharing service operator in Poland. The results of this algorithm are evaluated against scenarios without optimization that constitute a baseline and compared with the results of an exact algorithm to solve the Mixed Integer Programming (MIP) model. As performance metrics, the total travel time was used. Under identical conditions (number of vehicles, staff, and demand distribution), the average improvements with respect to the baseline of our algorithm and MIP solver were equal to 8.44\% and 19.6\% correspondingly. However, it should be noted that the MIP model also mimicked decisions on trip selection, which are excluded by current services business rules. The analysis of results suggests that, depending on the size of the workforce, the application of the proposed solution allows for improving performance metrics by roughly 3%-10%.

Police officers in San Bruno observed a Waymo car making an illegal U-turn. The incident highlights a gray area in law enforcement’s ability to cite robot cars when they violate traffic laws.

Abstract--Drones have recently emerged as a faster, safer, and cost-efficient way for last-mile deliveries of parcels, particularly for urgent medical deliveries highlighted during the pandemic. This paper addresses a new challenge of multi-parcel delivery with a swarm of energy-aware drones, accounting for time-sensitive customer requirements. Each drone plans an optimal multi-parcel route within its battery-restricted flight range to minimize delivery delays and reduce energy consumption. The problem is tackled by decomposing it into three sub-problems: (1) optimizing depot locations and service areas using K-means clustering; (2) determining the optimal flight range for drones through reinforcement learning; and (3) planning and selecting multi-parcel delivery routes via a new optimized plan selection approach. T o integrate these solutions and enhance long-term efficiency, we propose a novel algorithm leveraging actor-critic-based multi-agent deep reinforcement learning. Extensive experimentation using realistic delivery datasets demonstrate an exceptional performance of the proposed algorithm. We provide new insights into economic efficiency (minimize energy consumption), rapid operations (reduce delivery delays and overall execution time), and strategic guidance on depot deployment for practical logistics applications. Unmanned aerial vehicles (UA Vs), commonly known as drones, have gained significant attention as a solution for last-mile delivery, especially in recent years [1]. For instance, the COVID-19 pandemic has highlighted the vulnerabilities of traditional delivery methods, as deliverymen risk spreading the virus. This was particularly problematic in quarantine zones, where customers faced difficulties in accessing logistics services [2], [3]. In contrast, drones offer a safer and more flexible alternative. Due to their high mobility, carrying capacity, and accurate GPS navigation, drones are able to deliver parcels directly to small places such as doorways and balconies, avoiding human contact and traffic congestion.

Spatial networks are useful for modeling geographic phenomena where spatial interaction plays an important role. To analyze the spatial networks and their internal structures, graph-based methods such as community detection have been widely used. Community detection aims to extract strongly connected components from the network and reveal the hidden relationships between nodes, but they usually do not involve the attribute information. To consider edge-based interactions and node attributes together, this study proposed a family of GeoAI-enhanced unsupervised community detection methods called region2vec based on Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN). The region2vec methods generate node neural embeddings based on attribute similarity, geographic adjacency and spatial interactions, and then extract network communities based on node embeddings using agglomerative clustering. The proposed GeoAI-based methods are compared with multiple baselines and perform the best when one wants to maximize node attribute similarity and spatial interaction intensity simultaneously within the spatial network communities. It is further applied in the shortage area delineation problem in public health and demonstrates its promise in regionalization problems.

The development of mobility-on-demand services, rich transportation data sources, and autonomous vehicles (AVs) creates significant opportunities for shared-use AV mobility services (SAMSs) to provide accessible and demand-responsive personal mobility. SAMS fleet operation involves multiple interrelated decisions, with a primary focus on efficiently fulfilling passenger ride requests with a high level of service quality. This paper focuses on improving the efficiency and service quality of a SAMS vehicle fleet via anticipatory repositioning of idle vehicles. The rebalancing problem is formulated as a Markov Decision Process, which we propose solving using an advantage actor critic (A2C) reinforcement learning-based method. The proposed approach learns a rebalancing policy that anticipates future demand and cooperates with an optimization-based assignment strategy. The approach allows for centralized repositioning decisions and can handle large vehicle fleets since the problem size does not change with the fleet size. Using New York City taxi data and an agent-based simulation tool, two versions of the A2C AV repositioning approach are tested. The first version, A2C-AVR(A), learns to anticipate future demand based on past observations, while the second, A2C-AVR(B), uses demand forecasts. The models are compared to an optimization-based rebalancing approach and show significant reduction in mean passenger waiting times, with a slightly increased percentage of empty fleet miles travelled. The experiments demonstrate the model's ability to anticipate future demand and its transferability to cases unseen at the training stage.

In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communities exist, each defined by a unique task to be learned. In this setting, spatially distributed devices belonging to each community collaboratively contribute towards training their community model via wireless links provided by the UAV. Accordingly, the UAV acts as a mobile orchestrator coordinating the transmissions and the learning schedule among the devices in each community, intending to accelerate the learning process of all tasks. We propose a heuristic metric as a proxy for the training performance of the different tasks. Capitalizing on this metric, a surrogate objective is defined which enables us to jointly optimize the UAV trajectory and the scheduling of the devices by employing convex optimization techniques and graph theory. The simulations illustrate the out-performance of our solution when compared to other handpicked static and mobile UAV deployment baselines.

Uber Eats customers in California and Texas may soon have their takeout delivered by a driverless delivery pod after the company signed a 10-year deal with autonomous driving startup Nuro. Today's announcement is the culmination of over four years of start-and-stop negotiations between the two companies. Uber wanted to use Nuro's vehicles to make deliveries in Houston back in 2019, but those plans never panned out. Now, the two companies have struck a decade-long deal to expand robot deliveries to more customers than ever. Neither company would disclose the number of vehicles nor the expected number of customers who will participate in these early tests, but they did say they eventually hope to expand the service area to the greater Bay Area in California.

Waymo is now operating a robotaxi pilot in non-downtown San Francisco using Jaguar electric ... [ ] vehicles. This is territory where a real robotaxi make sense. Most of the biggest names in self-driving cars are aiming to make money selling Robotaxi service -- most quickly described as a self-driving Uber UBER -style service where you can summon a car with an app on your phone and ride elsewhere for a reasonable fee, possibly combined with "sharing" in some form, such as the style of UberPool or forms of on-demand transit. This is the plan of Waymo, Cruise, Amazon AMZN /Zoox, Argo AI and many others. It was obviously the plan of Uber ATG before it sold to Aurora, and Lyft LYFT L5 before it sold to Toyota.