Event-based vision is a promising technology with incredible potential for future space exploration. The Event-based Lunar Optical flow Egomotion estimation (ELOPE) Challenge aims at evaluating and comparing approaches for lunar landing egomotion estimation using data from a single event-based camera. This work is based on the ELOPE Dataset, which is the first publicly available event-based camera dataset for lunar landing. Over 44 teams participated, with 21 reaching the final leaderboard. After submitting 132 solutions, only the top three teams achieved performance surpassing the frame-based baseline. By focusing on realistic South Pole landing geometries and illumination conditions, the challenge directly targets guidance and navigation scenarios relevant to upcoming polar missions. The main contribution of this paper is the comparison of these top three competitors' submissions and a broader analysis of the main challenges in neuromorphic vision for autonomous lunar landing.

Intelligent devices for supporting persons with vision impairment are becoming more widespread, but they are lacking behind the advancements in intelligent driver assistant system. To make a first step forward, this work discusses the integration of the risk model technology, previously used in autonomous driving and advanced driver assistance systems, into an assistance device for persons with vision impairment. The risk model computes a probabilistic collision risk given object trajectories which has previously been shown to give better indications of an object's collision potential compared to distance or time-to-contact measures in vehicle scenarios. In this work, we show that the risk model is also superior in warning persons with vision impairment about dangerous objects. Our experiments demonstrate that the warning accuracy of the risk model is 67% while both distance and time-to-contact measures reach only 51% accuracy for real-world data.

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

This paper presents the real-world smart-meter dataset and offers an analysis of solutions derived from the Energy Prediction Technical Challenges, focusing primarily on two key competitions: the IEEE Computational Intelligence Society (IEEE-CIS) Technical Challenge on Energy Prediction from Smart Meter data in 2020 (named EP) and its follow-up challenge at the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) in 2021 (named as XEP). These competitions focus on accurate energy consumption forecasting and the importance of interpretability in understanding the underlying factors. The challenge aims to predict monthly and yearly estimated consumption for households, addressing the accurate billing problem with limited historical smart meter data. The dataset comprises 3,248 smart meters, with varying data availability ranging from a minimum of one month to a year. This paper delves into the challenges, solutions and analysing issues related to the provided real-world smart meter data, developing accurate predictions at the household level, and introducing evaluation criteria for assessing interpretability. Additionally, this paper discusses aspects beyond the competitions: opportunities for energy disaggregation and pattern detection applications at the household level, significance of communicating energy-driven factors for optimised billing, and emphasising the importance of responsible AI and data privacy considerations. These aspects provide insights into the broader implications and potential advancements in energy consumption prediction. Overall, these competitions provide a dataset for residential energy research and serve as a catalyst for exploring accurate forecasting, enhancing interpretability, and driving progress towards the discussion of various aspects such as energy disaggregation, demand response programs or behavioural interventions.

The shift towards renewable energy and decreasing battery prices have led to numerous installations of PV and battery systems in industrial and public buildings. Furthermore, the fluctuation of energy costs is increasing since energy sources based on solar and wind power depend on the weather situation. In order to reduce energy costs, it is necessary to plan energy-hungry activities while taking into account private PV production, battery capacity, and energy market prices. This problem was posed in the 2021 “IEEE-CIS Technical Challenge on Predict + Optimize for Renewable Energy Scheduling”. The target was to solve the two subtasks of forecasting the base load and of computing an optimal schedule of a list of energy intensive activities with inter-dependencies. We describe our approach to this challenge, which resulted in the third place of the leaderboard. For the prediction of the base load, we use a combination of a statistical and a machine learning approach. For the optimization of schedules, we employ a tuned mixed integer linear programming approach. We present a detailed experimental evaluation of the proposed approach on the use case and data provided in the challenge.

—Algorithms that involve both forecasting and opti- mization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traf- fic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the “IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling,” held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities

Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform various operators who can then plan risk mitigation measures. Such measures can be aided by the development of suitable machine learning (ML) models that predict, for example, the evolution of the collision risk over time. In October 2019, in an attempt to study this opportunity, the European Space Agency released a large curated dataset containing information about close approach events in the form of conjunction data messages (CDMs), which was collected from 2015 to 2019. This dataset was used in the Spacecraft Collision Avoidance Challenge, which was an ML competition where participants had to build models to predict the final collision risk between orbiting objects. This paper describes the design and results of the competition and discusses the challenges and lessons learned when applying ML methods to this problem domain.

The present report describes our approach, which yielded the 3rd place in the final leaderboard of the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling with a prediction error of 0.855737 and an energy cost score of 339160. For the load and PV predictions, we use simple straightforward approaches based on median values and machine learning, respectively. For the optimizations, we also use a standard approach, namely mixed integer linear programming, and applied different measures to improve its performance.

In this work, we present the SmartLobby, an intelligent environment system integrated into the lobby of a research institute. The SmartLobby is running 24/7, i.e. it can be used any time by anyone without any preparations. The goal of the system is to conduct research in the domain of human machine cooperation. One important first step towards this goal is a detailed human state modeling and estimation with head-eye-tracking as key component. The SmartLobby mainly integrates state-of-the-art algorithms that enable a thorough analysis of human behavior and state. These algorithms constitute the fundamental basis for the development of higher level system components. Here, we present our system with its various hardware and software components. Thereby, we focus on the head-eye-tracking as a key component to continuously observe persons using the system and customize content shown to them. The results of a multi-week lasting experiment demonstrate the effectiveness of the system.

Nowadays, autonomous lawn mowers are widely used in Europe. The robust autonomous operation and the ease of installation has lead to a substantial market share. Most autonomous lawn mowers move in a random fashion or with simple patterns because their self-localization capabilities are very limited. In this work, we analyze the potential of using apriori information about the shape of the boundary wire in combination with electromagnetic wire sensor readings for a particle-filter-based localization. In our experiments, this approach enables us to completely compensate for odometry drift. We achieve an accuracy of 20cm to 30cm in position and 3° in orientation for common garden sizes.

Active and semi-active suspension systems for vehicles became quite popular in the recent years as they allow for a smoother and safer ride compared to conventional suspension systems. The performance of an active/semi-active suspension system can be even more improved if the road condition in front of the vehicle is known. Currently only a few luxury cars combine fully active suspension with stereo cameras for such a predictive adaptation. However, we are not aware of any existing system for motorcycles. In this work, we present an algorithm that can cope with the rolling movement of a motorcycle. In addition, it can robustly reconstruct the road profile within a single time step and does not require temporal integration which allows real-time processing up to very high speeds at a precision in the order of millimeters. The complete system has been successfully tested on a German highway and a precise road laser scan has been used for evaluation.

Currently, the only mass-market service robots are floor cleaners and lawn mowers. Although available for more than 20 years, they mostly lack intelligent functions from modern robot research. In particular, the obstacle detection and avoidance is typically a simple physical collision detection. In this work, we discuss a prototype autonomous lawn mower with camera-based non-contact obstacle avoidance. We devised a low-cost compact module consisting of color cameras and an ARM-based processing board, which can be added to an autonomous lawn mower with minimal effort. For testing our system, we conducted a field test with 20 prototype units distributed in eight European countries with a total mowing time of 3,494 hours. The results show that our proposed system is able to work without expert interaction for a full season and strongly reduces collision events while still keeping the good mowing performance. Furthermore, a questionnaire with the testers revealed that most people would favor the camera-based mower over a non-camera-based mower.

Currently, the service robot market mainly consists of floor cleaning and lawn mowing robots. While some cleaning robots already feature SLAM technology for the constrained indoor application, autonomous lawn mowers typically use an electric wire for boundary definition and homing towards to charging station. An intermediate step towards SLAM for mowers is mapping of the boundary wire. In this work, we analyze three types of approaches for estimating the boundary of the working area of an autonomous mower: GNSS, visual odometry, and wheel-yaw odometry. We extended the latter with orientation loop closure, which gives the best overall result in estimating the metric shape of the boundary.

Block-Matching stereo is commonly used in applications with low computing resources in order to get some rough depth estimates. However, research on this simple stereo estimation technique has been very scarce since the advent of energy-based methods which promise a higher quality and a larger potential for further improvement. In the domain of intelligent vehicles, especially semi-global-matching (SGM) is widely spread due to its good performance and simple implementation. Unfortunately, the big downside of SGM is its large memory footprint because it is working on the full disparity space image. In contrast to this, local block-matching stereo is much more lean. In this paper, we will introduce a novel multi-block-matching scheme which tremendously improves the result of standard block-matching stereo while preserving the low memory-footprint and the low computational complexity. We tested our new multi-block-matching scheme on the KITTI stereo benchmark as well as on the new Middlebury stereo benchmark. For the KITTI benchmark we achieve results that even surpass the results of the best SGM implementations. For the new Middlebury benchmark we get results that are only slightly worse than state-of-the-art SGM implementations.