Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a one-vs-one binarization paradigm are enriched by one-vs-all correction classifiers. They are utilized to efficiently classify individual traffic participants and also identify hidden object classes which have not been presented to the classifiers during training. For each classifier of the ensemble an individual feature set is determined from a total set of 98 features. Thereby, the overall classification performance can be improved when compared to previous methods and, additionally, novel classes can be identified much more accurately. Furthermore, the proposed structure allows to give new insights in the importance of features for the recognition of individual classes which is crucial for the development of new algorithms and sensor requirements.Comment: 8 pages, 9 figures, accepted paper for 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, June 201

Radar-based Feature Design and Multiclass Classification for Road User Recognition

The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. Radar sensors provide an - with respect to well established camera systems - orthogonal way of measuring such scenes. In order to gain accurate classification results, 50 different features are extracted from the measurement data and tested on their performance. From these features a suitable subset is chosen and passed to random forest and long short-term memory (LSTM) classifiers to obtain class predictions for the radar input. Moreover, it is shown why data imbalance is an inherent problem in automotive radar classification when the dataset is not sufficiently large. To overcome this issue, classifier binarization is used among other techniques in order to better account for underrepresented classes. A new method to couple the resulting probabilities is proposed and compared to others with great success. Final results show substantial improvements when compared to ordinary multiclass classificationComment: 8 pages, 6 figure

Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices

Baseline generation for tracking applications is a difficult task when working with real world radar data. Data sparsity usually only allows an indirect way of estimating the original tracks as most objects' centers are not represented in the data. This article proposes an automated way of acquiring reference trajectories by using a highly accurate hand-held global navigation satellite system (GNSS). An embedded inertial measurement unit (IMU) is used for estimating orientation and motion behavior. This article contains two major contributions. A method for associating radar data to vulnerable road user (VRU) tracks is described. It is evaluated how accurate the system performs under different GNSS reception conditions and how carrying a reference system alters radar measurements. Second, the system is used to track pedestrians and cyclists over many measurement cycles in order to generate object centered occupancy grid maps. The reference system allows to much more precisely generate real world radar data distributions of VRUs than compared to conventional methods. Hereby, an important step towards radar-based VRU tracking is accomplished.Comment: 10 pages, 9 figures, accepted paper for 2019 20th International Radar Symposium (IRS), Ulm, Germany, June 2019. arXiv admin note: text overlap with arXiv:1905.1121

Using Machine Learning to Detect Ghost Images in Automotive Radar

Radar sensors are an important part of driver assistance systems and intelligent vehicles due to their robustness against all kinds of adverse conditions, e.g., fog, snow, rain, or even direct sunlight. This robustness is achieved by a substantially larger wavelength compared to light-based sensors such as cameras or lidars. As a side effect, many surfaces act like mirrors at this wavelength, resulting in unwanted ghost detections. In this article, we present a novel approach to detect these ghost objects by applying data-driven machine learning algorithms. For this purpose, we use a large-scale automotive data set with annotated ghost objects. We show that we can use a state-of-the-art automotive radar classifier in order to detect ghost objects alongside real objects. Furthermore, we are able to reduce the amount of false positive detections caused by ghost images in some settings

A Multi-Stage Clustering Framework for Automotive Radar Data

Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather conditions often makes them superior to other sensors such as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of the commonly used detection data level is a major challenge for subsequent signal processing. Therefore, the data points are often merged in order to form larger entities from which more information can be gathered. The merging process is often implemented in form of a clustering algorithm. This article describes a novel approach for first filtering out static background data before applying a twostage clustering approach. The two-stage clustering follows the same paradigm as the idea for data association itself: First, clustering what is ought to belong together in a low dimensional parameter space, then, extracting additional features from the newly created clusters in order to perform a final clustering step. Parameters are optimized for filtering and both clustering steps. All techniques are assessed both individually and as a whole in order to demonstrate their effectiveness. Final results indicate clear benefits of the first two methods and also the cluster merging process under specific circumstances.Comment: 8 pages, 5 figures, accepted paper for 2019 IEEE 22nd Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, October 201

Editorial: Household transport costs, economic stress and energy vulnerability

International audienceSince the early 2000s issues of transport poverty and social exclusion have received increasing attention in transport studies (Dodson et al., 2004; Hine and Mitchell, 2001; Lucas et al., 2001). Although much of this research has focused on low-mobility and/or carless individuals, there has been growing awareness that the costs of daily mobility can have important economic stress impacts. In developed countries with high levels of car dependence, the costs of motoring can be burdensome, raising questions of affordability for households with limited economic resources.A number of developments in the first two decades of this century have contributed to raise the profile of household transport costs as a research topic and a policy concern. First, and more obviously, increasing and increasingly volatile global oil prices have raised concerns for the vulnerability of households to fuel price increases (Dodson and Sipe, 2007). Second, the rise of the climate change agenda has led to consider pricing measures as a key component of sustainable transport policy. Implementation of such measures however, has often been hampered by concerns for the distributional impacts of increasing transport costs faced by households. Third, the global financial crisis of 2007–2008 and its aftermath have highlighted broader issues of living standards, economic stress and affordability, which go beyond the specific case of transport.In this context, a further reason to investigate household transport costs has to do with other competing pressures on household budgets

Transport poverty and fuel poverty in the UK: From analogy to comparison

The notion of ’fuel poverty’, referring to affordable warmth, underpins established research and policy agendas in the UK and has been extremely influential worldwide. In this context, British researchers, official policymaking bodies and NGOs have put forward the notion of ’transport poverty’, building on an implicit analogy between (recognised) fuel poverty and (neglected) transport affordability issues. However, the conceptual similarities and differences between ’fuel’ and ’transport’ poverty remain largely unaddressed in the UK. This paper systematically compares and contrasts the two concepts, examining critically the assumption of a simple equivalence between them. We illustrate similarities and differences under four headings: (i) negative consequences of lack of warmth and lack of access; (ii) drivers of fuel and transport poverty; (iii) definition and measurement; (iv) policy interventions. Our review suggests that there are important conceptual and practical differences between transport and domestic energy consumption, with crucial consequences for how affordability problems amongst households are to be conceptualised and addressed. In a context where transport and energy exhibit two parallel policy worlds, the analysis in the paper and these conclusions reinforce how and why these differences matter. As we embark on an ever closer union between our domestic energy and transport energy systems the importance of these contradictions will become increasingly evident and problematic. This work contributes to the long-term debate about how best to manage these issues in a radical energy transition that properly pays attention to issues of equity and affordability