High-definition maps (HD maps) are a key component of most modern self-driving systems due to their valuable semantic and geometric information. Unfortunately, building HD maps has proven hard to scale due to their cost as well as the requirements they impose in the localization system that has to work everywhere with centimeter-level accuracy. Being able to drive without an HD map would be very beneficial to scale self-driving solutions as well as to increase the failure tolerance of existing ones (e.g., if localization fails or the map is not up-to-date). Towards this goal, we propose MP3, an end-to-end approach to mapless driving where the input is raw sensor data and a high-level command (e.g., turn left at the intersection). MP3 predicts intermediate representations in the form of an online map and the current and future state of dynamic agents, and exploits them in a novel neural motion planner to make interpretable decisions taking into account uncertainty. We show that our approach is significantly safer, more comfortable, and can follow commands better than the baselines in challenging long-term closed-loop simulations, as well as when compared to an expert driver in a large-scale real-world dataset.

Traditional online maps, widely used on Internet such as Google map and Baidu map, are rendered from vector data. Timely updating online maps from vector data, of which the generating is time-consuming, is a difficult mission. It is a shortcut to generate online maps in time from remote sensing images, which can be acquired timely without vector data. However, this mission used to be challenging or even impossible. Inspired by image-to-image translation (img2img) techniques based on generative adversarial network (GAN), we propose a semi-supervised structure-augmented online map GAN (S$^{2}$OMGAN) model to generate online maps directly from remote sensing images. In this model, we designed a semi-supervised learning strategy to pre-train S$^{2}$OMGAN on rich unpaired samples and finetune it on limited paired samples in reality. We also designed image gradient L1 loss and image gradient structure loss to generate an online map with global topological relationship and detailed edge curves of objects, which are important in cartography. Moreover, we propose edge structural similarity index (ESSI) as a metric to evaluate the quality of topological consistency between generated online maps and ground truths. Experimental results present that S$^{2}$OMGAN outperforms state-of-the-art (SOTA) works according to mean squared error, structural similarity index and ESSI. Also, S$^{2}$OMGAN wins more approval than SOTA in the human perceptual test on visual realism of cartography. Our work shows that S$^{2}$OMGAN is potentially a new paradigm to produce online maps. Our implementation of the S$^{2}$OMGAN is available at \url{https://github.com/imcsq/S2OMGAN}.

Researchers have uncovered a 1,500-year-old stucco mask of Maya ruler K'inich Janaab'Pakal. What differentiates this mask from others is it's seemingly made in the king's likeness. An eagle-eyed archaeologist has used a freely available online map to locate 27 Maya ceremonial sites in Mexico. Takeshi Inomata, a professor of archaeology at the University of Arizona, made the discovery using a LiDAR (Light Detection and Ranging) map he found online last year, according to the New York Times. LiDAR technology harnesses a laser to measure distances to the Earth's surface and can prove extremely valuable to study what is hidden in areas with thick vegetation.

A lot has changed since Engadget was born, both in the gadgets we use and what we do with them on a regular basis. When the site started in 2004, fitness trackers, voice assistants and electric cars were the stuff of fiction. Now most of these are commonplace, so much so that we put our trust in them on a daily basis. To celebrate Engadget's 15th birthday, here are 15 things that didn't exist 15 years ago. In the past, mobile phones could do mostly two things: Make calls and send (and receive) text messages. If you had a fancier model, you could also play a little game. Features were added over the years, including music playback and photography. Smartphones like the BlackBerry and the Palm Treo that combined the functionality of PDAs and phones eventually came about, but were mostly designed for business users.

Abstract--In this work, we present a novel strategy for correcting imperfections in occupancy grid maps called map decay. The objective of map decay is to correct invalid occupancy probabilities of map cells that are unobservable by sensors. The strategy was inspired by an analogy between the memory architecture believed to exist in the human brain and the maps maintained by an autonomous vehicle. It consists in merging sensory information obtained during runtime (online) with a priori data from a high-precision map constructed offline. In map decay, cells observed by sensors are updated using traditional occupancy grid mapping techniques and unobserved cells are adjusted so that their occupancy probabilities tend to the values found in the offline map. This strategy is grounded in the idea that the most precise information available about an unobservable cell is the value found in the high-precision offline map. Map decay was successfully tested and is still in use in the IARA autonomous vehicle from Universidade Federal do Espírito Santo. The brain allows humans to operate in highly dynamic and complex environments, and to solve general purpose problems. The idea of giving these abilities to artificial entities by reproducing the brain's cognitive processes always fascinated researchers.