•Men are more likely to associate positive emotions towards automated cars.•Women are more likely to associate negative emotions towards automated cars.•These findings partially explain sex ...differences in the willingness to use them.•These findings cannot be explained by age or education effects.•Age moderates the effect of biological sex on willingness to use through anxiety.
Current research on willingness to use automated cars indicates differences between men and women, with the latter group showing lower usage intentions. This study aims at providing a first explanation of this effect. Research from other fields suggests that affective reactions might be able to explain behavioral intentions and responses towards technology, and that these affects vary depending on age levels. By examining a sample of 1603 participants representative for Germany (in terms of biological sex, age, and education) we found evidence that affective responses towards automotive cars (i.e., anxiety and pleasure) explain (i.e., mediate) the effect of biological sex on willingness to use them. Moreover, we found that these emotional processes vary as a function of respondent age in such a way that the differential effect of sex on anxiety (but not on pleasure) was more pronounced among relatively young respondents and decreased with participants’ age. Our results suggest that addressing anxiety-related responses towards automated cars (e.g., by providing safety-related information) and accentuating especially the pleasurable effects of automated cars (e.g., via advertising) reduce differences between men and women. Addressing the anxiety-related effects in order to reduce sex differences in usage intentions seems to be less relevant for older target groups, whereas promoting the pleasurable responses is equally important across age groups.
We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car ...classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download.
Self-driving cars: A survey Badue, Claudine; Guidolini, Rânik; Carneiro, Raphael Vivacqua ...
Expert systems with applications,
03/2021, Volume:
165
Journal Article
Peer reviewed
We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be ...categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espírito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.
•Recently developments of autonomous driving from academic and industry point of view.•Breakdown of the main aspects comprising autonomous driving and their evolution.•Autonomous driving architecture review and proposal.
We propose an end-to-end machine learning model that integrates multi-task (MT) learning, convolutional neural networks (CNNs), and control algorithms to achieve efficient inference and stable ...driving for self-driving cars. The CNN-MT model can simultaneously perform regression and classification tasks for estimating perception indicators and driving decisions, respectively, based on the direct perception paradigm of autonomous driving. The model can also be used to evaluate the inference efficiency and driving stability of different CNNs on the metrics of CNN’s size, complexity, accuracy, processing speed, and collision number, respectively, in a dynamic traffic. We also propose new algorithms for controllers to drive a car using the indicators and its short-range sensory data to avoid collisions in real-time testing. We collect a set of images from a camera of The Open Racing Car Simulator in various driving scenarios, train the model using this dataset, test it in unseen traffics, and find that it outperforms earlier models in highway traffic. The stability of end-to-end learning and self driving depends crucially on the dynamic interplay between CNN and control algorithms. The source code and data of this work are available on our website, which can be used as a simulation platform to evaluate different learning models on equal footing and quantify collisions precisely for further studies on autonomous driving.
Electric vehicles (EVs) generally use an electric heating system to provide heat. However, the heating system consumes a large amount of energy, and therefore reduces the mileage of the vehicle. The ...energy consumption can be reduced by replacing the electric heating system with a heat pump air conditioning system. Such systems achieve an effective heating of the vehicle interior, but do not provide a defog or dehumidification function. Consequently, the inside surface of the windshield tends to fog in cold weather; leading to poor driver visibility and an impaired road safety. Accordingly, the present study proposes a novel high-efficiency heating, ventilation and air conditioning (HVAC) system with both heating and defog/dehumidification functions for electric vehicles. The effectiveness of the proposed system is investigated experimentally using a simulated cabin placed in a temperature and humidity-controlled test chamber. The experimental results confirm that the HVAC system achieves the required cooling, heating and defog/dehumidification functions and meets the corresponding standards. Moreover, the application of HVAC in EVs could lead to significant electrical power saving effect.
Simultaneous pure-rotational coherent anti-Stokes Raman spectroscopy (PRCARS) and vibrational O2 CARS spectroscopy (VCARS) were performed at elevated pressure and lowered temperature conditions in ...non-reacting compressible flow. We applied dual-pump CARS in a three-laser, three-color configuration to simultaneously acquire the PRCARS and VCARS spectra of O2. PRCARS spectra provide excellent sensitivity to temperature at relatively low temperatures. Pressure was extracted using the differential response of collisional effects in the PRCARS and the VCARS spectra. We used an under-expanded jet outside a choked converging nozzle as the compressible flow-field. We numerically analyze the pressure sensitivity of the combined CARS technique. Finally, we compare the collisional narrowing lineshape models of rotational diffusion narrowing and modified-exponential-gap model, for fitting the experimental spectrum.
Graphical Abstract
To simulate car-following behaviors better when the traffic light is red, three successive car-following data at a signalized intersection of Jinan in China were collected by using a new proposed ...data acquisition method and then analyzed to select input variables of the extended car-following model. An extended car-following model considering two leading cars’ accelerations was proposed, calibrated and verified with field data obtained on the basis of the full velocity difference model and then a comparative model used for comparative research was also proposed and calibrated in the light of the GM model. The results indicate that the extended car-following model could fit measured data well, and that the fitting precision of the extended model is prior to the comparative model, whose mean absolute error is reduced by 22.83%. Finally a theoretical car-following model considering multiple leading cars’ accelerations was put forward which has potential applicable to vehicle automation system and vehicle safety early warning system, and then the linear stability analysis and numerical simulations were conducted to analyze some observed physical features existing in the realistic traffic.
•Three successive car-following data at a signalized intersection of Jinan in China were collected.•A car-following model considering two leading cars’ accelerations was proposed and calibrated.•A comparative model used for comparative research was also proposed and calibrated.•The fitting precision of the extended model is prior to the comparative model.•Finally a theoretical model considering multiple leading cars’ accelerations was put forward.
Autonomuous transportation systems require navigation performance with a high level of integrity. As Global Navigation Satellite System (GNSS) real-time kinematic (RTK) solutions are needed to ensure ...lane level accuracy of the whole system, these solutions should be trustworthy, which is often not the case in urban environments. Thus, the prediction of integrity for specific routes or trajectories is of interest. The carrier-to-noise density ratio (C/Nsub.0) reported by the GNSS receiver offers important insights into the signal quality, the carrier phase availability and subsequently the RTK solution integrity. The ultimate goal of this research is to investigate the predictability of the GNSS signal strength. Using a ray-tracing algorithm together with known satellite positions and 3D building models, not only the satellite visibility but also the GNSS signal propagation conditions at waypoints along an intended route are computed. Including antenna gain, free-space propagation as well as reflection and diffraction at surfaces and vegetation, the predicted C/Nsub.0 is compared to that recorded by an Septentrio Altus receiver during an experiment in an urban environment in Hannover. Although the actual gain pattern of the receiving antenna was unknown, good agreements were found with small offsets between measured and predicted C/Nsub.0.
The intensive motorization growth observed in emerging and developing economies has attracted increased academic attention. However, many existing studies frequently investigate the car ownership ...determinants that are typical of Western countries and use aggregate measures that mask the role of imported used cars. This implies that there is an important research gap concerning the role of the second-hand vehicles as a source of car ownership growth in emerging and developing countries. This paper aims to reveal the dichotomous character of car ownership growth in an emerging economy and identify the determinants of local primary (new cars) and secondary (imported used cars) car markets. Using data from the Polish Central Vehicle Register containing entries for more than 20 million cars registered and applying the spatial regression models, we disclose that in addition to well-known determinants of car ownership growth, such as income, population density, and housing types, there may be other factors specific to emerging economies driving this process. Specifically, we test the influence of geographical distance on the source of the car supply and the number of companies and entrepreneurs importing and repairing used cars. The findings suggest that future investigations of motorization processes concerning developing and emerging economies should consider the scale of second-hand car imports and its impact on car ownership and seek country-specific determinants of the phenomenon.
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![Predicting C/N[sub.0] as a ...](http://api.summon.serialssolutions.com/2.0.0/image/issn/XR4PS5YY4K/2072-4292/small "Predicting C/N[sub.0] as a Key Parameter for Network RTK Integrity Prediction in Urban Environments")
