In the k-Kemeny problem, we are given an ordinal election, i.e., a collection of votes ranking the candidates from best to worst, and we seek the smallest number of swaps of adjacent candidates that ensure that the election has at most k different rankings. We study this problem for a number of structured domains, including the single-peaked, single-crossing, group-separable, and Euclidean ones. We obtain two kinds of results: (1) We show that k-Kemeny remains intractable under most of these domains, even for k=2, and (2) we use k-Kemeny to rank these domains in terms of their diversity.

Most current end-to-end (E2E) autonomous driving algorithms are built on standard vehicles in structured transportation scenarios, lacking exploration of robot navigation for unstructured scenarios such as auxiliary roads, campus roads, and indoor settings. This paper investigates E2E robot navigation in unstructured road environments. First, we introduce two data collection pipelines - one for real-world robot data and another for synthetic data generated using the Isaac Sim simulator, which together produce an unstructured robotics navigation dataset -- FreeWorld Dataset. Second, we fine-tuned an efficient E2E autonomous driving model -- VAD -- using our datasets to validate the performance and adaptability of E2E autonomous driving models in these environments. Results demonstrate that fine-tuning through our datasets significantly enhances the navigation potential of E2E autonomous driving models in unstructured robotic environments. Thus, this paper presents the first dataset targeting E2E robot navigation tasks in unstructured scenarios, and provides a benchmark based on vision-based E2E autonomous driving algorithms to facilitate the development of E2E navigation technology for logistics and service robots. The project is available on Github.

Autonomous vehicles require road information for their operation, usually in form of HD maps. Since offline maps eventually become outdated or may only be partially available, online HD map construction methods have been proposed to infer map information from live sensor data. A key issue remains how to exploit such partial or outdated map information as a prior. We introduce M3TR (Multi-Masking Map Transformer), a generalist approach for HD map construction both with and without map priors. We address shortcomings in ground truth generation for Argoverse 2 and nuScenes and propose the first realistic scenarios with semantically diverse map priors. Examining various query designs, we use an improved method for integrating prior map elements into a HD map construction model, increasing performance by +4.3 mAP. Finally, we show that training across all prior scenarios yields a single Generalist model, whose performance is on par with previous Expert models that can handle only one specific type of map prior. M3TR thus is the first model capable of leveraging variable map priors, making it suitable for real-world deployment. Code is available at https://github.com/immel-f/m3tr

Online High Definition Map (HDMap) estimation from sensors offers a low-cost alternative to manually acquired HDMaps. As such, it promises to lighten costs for already HDMap-reliant Autonomous Driving systems, and potentially even spread their use to new systems. In this paper, we propose to improve online HDMap estimation by accounting for already existing maps. We identify 3 reasonable types of useful existing maps (minimalist, noisy, and outdated). We also introduce MapEX, a novel online HDMap estimation framework that accounts for existing maps. MapEX achieves this by encoding map elements into query tokens and by refining the matching algorithm used to train classic query based map estimation models. We demonstrate that MapEX brings significant improvements on the nuScenes dataset. For instance, MapEX - given noisy maps - improves by 38% over the MapTRv2 detector it is based on and by 16% over the current SOTA.

This article will guide the step-by-step process to publish a web app featuring split panels. The web app is created using the Earth Engine Cloud-computing platform. Earth Engine makes tons of satellite images available to analyze and display. It also provides web app publication. The web app we are going to discuss today is split panels.

A robot understands its world through the raw information it senses from its surroundings. This raw information is not suitable as a shared representation between the robot and its user. A semantic map, containing high-level information that both the robot and user understand, is better suited to be a shared representation. We use the semantic map as the user-facing interface on our fleet of floor-cleaning robots. Jitter in the robot's sensed raw map, dynamic objects in the environment, and exploration of new space by the robot are common challenges for robots. Solving these challenges effectively in the context of semantic maps is key to enabling semantic maps for lifelong mapping. First, as a robot senses new changes and alters its raw map in successive runs, the semantics must be updated appropriately. We update the map using a spatial transfer of semantics. Second, it is important to keep semantics and their relative constraints consistent even in the presence of dynamic objects. Inconsistencies are automatically determined and resolved through the introduction of a map layer of meta-semantics. Finally, a discovery phase allows the semantic map to be updated with new semantics whenever the robot uncovers new information. Deployed commercially on thousands of floor-cleaning robots in real homes, our user-facing semantic maps provide a intuitive user experience through a lifelong mapping robot.

The cubicle is making a comeback. As thousands of companies contemplate restarting operations, executives are weighing how best to reconfigure workspaces that have, by and large, been designed to minimize cost and foster the face-to-face interactions that can spread the deadly coronavirus. Some companies are looking at high-tech approaches to enforce social distancing and track interactions, with location-monitoring apps and badges, artificial intelligence surveillance cameras, and high-tech health checks. Other innovations will be simpler: stickers to enforce 6 feet of distance between coworkers; staggered shifts that allow for more spacing; more regular cleanings; and of course oodles of hand sanitizer. But one of the most important innovations may turn out to be cardboard or plastic dividers that turn open-plan offices into something more reminiscent of the 1980s.

The National Transportation Safety Board held a hearing on Tuesday regarding a deadly 2018 crash in which a Tesla Model X slammed into a Mountain View highway divider at 70mph, was subsequently struck by two other vehicles and then exploded. During that announcement, the safety board revealed that the driver, Apple developer Walter Huang, was playing a mobile game on his phone at the time of the accident, while the vehicle's Autopilot feature was engaged. "Government regulators have provided scant oversight" over the semi-autonomous driving systems that are quickly becoming standard features on modern automobiles, NTSB chair Robert Sumwalt declared. While the NTSB does not have the authority to enforce safety measures, the National Highway Traffic Safety Administration can issue recalls for unsafe vehicle tech. The NTSB also determined via cellphone records and device data that Huang's phone was running a mobile game at the time of the crash.

Computer Vision Guided Deep Learning Network & Machine Learning Techniques to build Fully-Functional Autonomous Vehicles. "If you recognize that self-driving cars are going to prevent accidents, AI will help to reduce one of the leading causes of death in the world." If ride-on-demand services such as Uber & Ola have made a revolution in the idea of conveyance, self-driving vehicles are going to be the next renaissance shaking up the whole transportation industry. This new idea is on its way to become a multi trillion-dollar business -- bigger than Amazon and Walmart combined. According to the World Economic Forum, this big leap in the auto industry will deliver $3.1 trillion annually by reducing number of crashes, need for emergency services, saving man-hours, cost of car ownership & also indirect savings from shorter commutes and less carbon emissions. On top of that, there are endless design possibilities, once you eliminate the need for a steering wheel and a driver.

Tesla loves a good fight. CEO Elon Musk has battled car dealers, President Trump, and more than a few reporters. Now he has found a new opponent, in the National Transportation Safety Board. The agency is investigating the crash of a Model X that was running with Autopilot engaged when it slammed into a highway divider in Northern California last month, killing the driver. Today, the NTSB announced it kicked Tesla off the team looking into what happened, and how to stop it recurring.