Conservative Estimation of Perception Relevance of Dynamic Objects for Safe Trajectories in Automotive Scenarios

Having efficient testing strategies is a core challenge that needs to be overcome for the release of automated driving. This necessitates clear requirements as well as suitable methods for testing. In this work, the requirements for perception modules are considered with respect to relevance. The concept of relevance currently remains insufficiently defined and specified. In this paper, we propose a novel methodology to overcome this challenge by exemplary application to collision safety in the highway domain. Using this general system and use case specification, a corresponding concept for relevance is derived. Irrelevant objects are thus defined as objects which do not limit the set of safe actions available to the ego vehicle under consideration of all uncertainties. As an initial step, the use case is decomposed into functional scenarios with respect to collision relevance. For each functional scenario, possible actions of both the ego vehicle and any other dynamic object are formalized as equations. This set of possible actions is constrained by traffic rules, yielding relevance criteria. As a result, we present a conservative estimation which dynamic objects are relevant for perception and need to be considered for a complete evaluation. The estimation provides requirements which are applicable for offline testing and validation of perception components. A visualization is presented for examples from the highD dataset, showing the plausibility of the results. Finally, a possibility for a future validation of the presented relevance concept is outlined

SURE-Val: Safe Urban Relevance Extension and Validation

To evaluate perception components of an automated driving system, it is necessary to define the relevant objects. While the urban domain is popular among perception datasets, relevance is insufficiently specified for this domain. Therefore, this work adopts an existing method to define relevance in the highway domain and expands it to the urban domain. While different conceptualizations and definitions of relevance are present in literature, there is a lack of methods to validate these definitions. Therefore, this work presents a novel relevance validation method leveraging a motion prediction component. The validation leverages the idea that removing irrelevant objects should not influence a prediction component which reflects human driving behavior. The influence on the prediction is quantified by considering the statistical distribution of prediction performance across a large-scale dataset. The validation procedure is verified using criteria specifically designed to exclude relevant objects. The validation method is successfully applied to the relevance criteria from this work, thus supporting their validity.Comment: Accepted at Uni-DAS e.V. Workshop Fahrerassistenz und automatisiertes Fahre

Inferring choice criteria with mixture IRT models: A demonstration using ad hoc and goal-derived categories

Whether it pertains to the foods to buy when one is on a diet, the items to take along to the beach on one’s day off or (perish the thought) the belongings to save from one’s burning house, choice is ubiquitous. We aim to determine from choices the criteria individuals use when they select objects from among a set of candidates. In order to do so we employ a mixture IRT (item-response theory) model that capitalizes on the insights that objects are chosen more often the better they meet the choice criteria and that the use of different criteria is reflected in inter-individual selection differences. The model is found to account for the inter-individual selection differences for 10 ad hoc and goal-derived categories. Its parameters can be related to selection criteria that are frequently thought of in the context of these categories. These results suggest that mixture IRT models allow one to infer from mere choice behavior the criteria individuals used to select/discard objects. Potential applications of mixture IRT models in other judgment and decision making contexts are discussed

Whistleblowing in Science: in the Lion's Den

With the growing emphasis on scientific integrity as a way to curb research misconduct, the “moral obligation to act” when a researcher observes a breach of integrity is becoming stronger. However, it takes a lot of courage to blow the whistle as it can have an enormous impact on the whistleblower’s career and personal well-being – and that of colleagues and researchers associated with the perpetrator. Therefore, it is important to be cognizant of the stressful process that accompanies the act of whistleblowing, to provide clear and accessible procedures to report misconduct, and to support whistleblowers throughout the process. Furthermore, it is essential that appropriate whistleblower protection measures are in place and enforced. Based on our review, we argue that an obligation to report scientific misconduct would currently do more harm than good

Predicting Lexical Norms: A Comparison between a Word Association Model and Text-Based Word Co-occurrence Models

In two studies we compare a distributional semantic model derived from word co-occurrences and a word association based model in their ability to predict properties that affect lexical processing. We focus on age of acquisition, concreteness, and three affective variables, namely valence, arousal, and dominance, since all these variables have been shown to be fundamental in word meaning. In both studies we use a model based on data obtained in a continued free word association task to predict these variables. In Study 1 we directly compare this model to a word co-occurrence model based on syntactic dependency relations to see which model is better at predicting the variables under scrutiny in Dutch. In Study 2 we replicate our findings in English and compare our results to those reported in the literature. In both studies we find the word association-based model fit to predict diverse word properties. Especially in the case of predicting affective word properties, we show that the association model is superior to the distributional model

A comparison of the Spatial Arrangement Method and the Total-Set Pairwise Rating Method for obtaining similarity data in the conceptual domain

We compare two methods for obtaining similarity data in the conceptual domain. In the Spatial Arrangement Method (SpAM), participants organize stimuli on a computer screen so that the distance between stimuli represents their perceived dissimilarity. In Total-Set Pairwise Rating Method (PRaM), participants rate the (dis)similarity of all pairs of stimuli on a Likert scale. In each of three studies, we had participants indicate the similarity of four sets of conceptual stimuli with either PRaM or SpAM. Studies 1 and 2 confirm two caveats that have been raised for SpAM. (i) While SpAM takes significantly less time to complete than PRaM, it yields less reliable data than PRaM does. (ii) Because of the spatial manner in which similarity is measured in SpAM, the method is biased against feature representations. Despite these differences, averaging SpAM and PRaM dissimilarity data across participants yields comparable aggregate data. Study 3 shows that by having participants only judge half of the pairs in PRaM, its duration can be significantly reduced, without affecting the dissimilarity distribution, but at the cost of a smaller reliability. Having participants arrange multiple subsets of the stimuli does not do away with the spatial bias of SpAM

When cheating is an honest mistake

Dishonesty is an intriguing phenomenon, studied extensively across various disciplines due to its impact on people’s lives as well as society in general. To examine dishonesty in a controlled setting, researchers have developed a number of experimental paradigms. One of the most popular approaches in this regard, is the matrix task, in which participants receive matrices wherein they have to find two numbers that sum to 10 (e.g., 4.81 and 5.19), under time pressure. In a next phase, participants need to report how many matrices they had solved correctly, allowing them the opportunity to cheat by exaggerating their performance in order to get a larger reward. Here, we argue, both on theoretical and empirical grounds, that the matrix task is ill-suited to study dishonest behavior, primarily because it conflates cheating with honest mistakes. We therefore recommend researchers to use different paradigms to examine dishonesty, and treat (previous) findings based on the matrix task with due caution

A probabilistic threshold model: Analyzing semantic categorization data with the Rasch model

According to the Threshold Theory (Hampton, 1995, 2007) semantic categorization decisions come about through the placement of a threshold criterion along a dimension that represents items' similarity to the category representation. The adequacy of this theory is assessed by applying a formalization of the theory, known as the Rasch model (Rasch, 1960; Thissen & Steinberg, 1986), to categorization data for eight natural language categories and subjecting it to a formal test. In validating the model special care is given to its ability to account for inter- and intra-individual differences in categorization and their relationship with item typicality. Extensions of the Rasch model that can be used to uncover the nature of category representations and the sources of categorization differences are discussed

Development of a Low-Resource Combined Gamma-Ray and Neutron Spectrometer for Planetary Science

Planetary neutron and gamma-ray spectroscopy (NGRS) has become a standard technique to measure distinctive geochemical composition and volatile abundance signatures for key elements relevant to planetary structure and evolution. Previous NGRS measurements have led to the discovery of the concentration of many elements including hydrogen on the Moon, Mars, Mercury, and the asteroids Eros, Vesta, and Ceres, but by utilizing separate NGRS. We have developed the Elpasolite Planetary Ice and Composition Spectrometer (EPICS) instrument, an innovative and combined NGRS with low resource requirements. EPICS incorporates elpasolite scintillator read out by silicon photomultipliers (SiPMs) to provide significant reduction in size, weight, and power, while achieving excellent neutron detection sensitivity and gamma-ray energy resolution as good as 2.9% full-width half-maximum at 662 keV. EPICS is ideally suited to resource constrained missions and is applicable to numerous targets such as the Moon, Mars, and small planetary bodies. An overview of the EPICS instrument and its simulated performance on a few notional missions is presented. We have integrated and done performance testing of a prototype of the EPICS instrument, including optimization of an amplification and summing circuit for a 64-element SiPM array that preserves pulse shape discrimination capability, which will be summarized