Vision-based lane keeping is a topic of significant interest in the robotics and autonomous ground vehicles communities in various on-road and off-road applications. The skid-steered vehicle architecture has served as a useful vehicle platform for human controlled operations. However, systematic modeling, especially of the skid-slip wheel terrain interactions (primarily in off-road settings) has created bottlenecks for automation deployment. End-to-end learning based methods such as imitation learning and deep reinforcement learning, have gained prominence as a viable deployment option to counter the lack of accurate analytical models. However, the systematic formulation and subsequent verification/validation in dynamic operation regimes (particularly for skid-steered vehicles) remains a work in progress. To this end, a novel approach for structured formulation for learning visual navigation is proposed and investigated in this work. Extensive software simulations, hardware evaluations and ablation studies now highlight the significantly improved performance of the proposed approach against contemporary literature.

Nature is not a blocker to housing growth and the government risks missing both its housing and nature targets if it views it as one, a cross-party group of MPs has warned in a new report. The Planning and Infrastructure Bill overrides existing habitat protections, which the government has suggested is a barrier to its target to build 1.5 million houses by the end of this parliament. But in a report published on Sunday, the Environmental Audit Committee (EAC) found the measures outlined in the bill are not enough to allow the government to meet its goals. Using nature as a scapegoat means that the government will be less effective at tackling some of the genuine challenges facing the planning system, the report said. A Ministry of Housing spokesperson said it was fixing a failing system with landmark reforms, which would deliver a win-win for the economy and the environment.

How Google's DeepMind tool is'more quickly' forecasting hurricane behavior'Less expensive and time consuming' model helps with fast and accurate predictions, possibly saving lives and property When then Tropical Storm Melissa was churning south of Haiti, Philippe Papin, a National Hurricane Center (NHC) meteorologist, had confidence it was about to grow into a monster hurricane. As the lead forecaster on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and begin a turn towards the coast of Jamaica. No NHC forecaster had ever issued such a bold forecast for rapid strengthening. But Papin had an ace up his sleeve: artificial intelligence in the form of Google's new DeepMind hurricane model - released for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Ukrainian President Volodymyr Zelensky has vowed to overhaul state-owned energy companies, after a major corruption scandal engulfed the country's energy sector. Around $100 million (£76m) has been embezzled, anti-graft investigators said, causing outrage in a country where Russian attacks have resulted in crippling power outages. Alongside a full audit of their financial activities, the management of these companies is to be renewed, Zelensky wrote in a post on X on Saturday. Energoatom, the state nuclear company at the heart of the scandal, will have a new supervisory board within a week, he added. Several of those implicated in the scandal have close links to the Ukrainian president.

The Aedes aegypti mosquito that can carry dengue, yellow fever, and Zika was thought to be too reliant on a hot and wet climate to survive in the Mountain West. But now, a population is thriving in Western Colorado. Hannah Livesay, biologist at the Grand River Mosquito Control District, points out the characteristic white markings of an Aedes aegypti mosquito shown under a microscope at her lab in Grand Junction, Colo. It can carry life-threatening diseases. It's difficult to find and hard to kill.

This becomes more obvious when studying a particular disease, for instance. Also, gaining access to large-scale datasets is often difficult due to privacy concerns.

Open problem: "Can we extend [fully dynamic] results (algorithm or hardness) to non-monotone

Suppose we are considering a multivariate event sequence. What caused a specific event to happen? Which variables are causes of each other?