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.
•Handling Pedestrians in Self-Driving Cars using Image Tracking and Frenét Frames.•The method is safer and more efficient than systems without tracking functionality.•Tracking pedestrians enables ...early decision capability.•Our self-driving car was evaluated in both simulated and real-world scenarios.
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The development of intelligent autonomous cars is of great interest. A particular and challenging problem is to handle pedestrians, for example, crossing or walking along the road. Since pedestrians are one of the most fragile elements in traffic, a reliable pedestrian detection and handling system is mandatory. The current pedestrian handling system of our autonomous cars suffers from the limitation of the pure detection-based systems, i.e., it limits the autonomous car system to make decisions based only on the very present moment. This work improves the pedestrian handling systems by incorporating an object tracker with the aim of predicting the pedestrian’s behavior. With this knowledge, the autonomous car can better decide the time to stop and to start moving, providing a more comfortable, efficient, and safer driving experience. The proposed method was augmented with a path generator, based on Frenét Frames, and incorporated to our self-driving car in order to enable a better decision making and to enable overtaking pedestrians. The behaviour of our self-driving car was evaluated in both simulated and real-world scenarios. Results showed the proposed system is safer and more efficient than the system without tracking functionality due to the early decision capability.
The ambulatory arterial stiffness index (AASI), derived from ambulatory blood pressure (BP) monitoring recordings, is an indirect marker of arterial stiffness and a potential predictor of ...cardiovascular risk. Resistant hypertension is defined as uncontrolled office BP despite the use of at least three antihypertensive drugs. The aim of this prospective study was to investigate the AASI prognostic value in patients with resistant hypertension.
At baseline, 547 patients underwent clinical-laboratory, and 24-h ambulatory BP monitoring examinations. AASI was defined as 1 minus the regression slope of DBP on SBP, and was calculated by standard and symmetric regression. Primary endpoints were a composite of fatal and nonfatal cardiovascular events and all-cause and cardiovascular mortalities. Multiple Cox regression was used to assess associations between AASI and subsequent endpoints.
After median follow-up of 4.8 years, 101 patients (18.4%) reached the primary endpoint, and 65 all-cause deaths (11.9%) occurred (45 from cardiovascular causes). 24-h AASI was the best independent predictor of composite endpoint (hazard ratio 1.46, 95% confidence interval 1.12-1.92, for increments of 1-SD = 0.14), whereas cardiovascular mortality was best predicted by night-time AASI (hazard ratio 1.73, 95% confidence interval 1.13-2.65), after adjustments for cardiovascular risk factors, including mean ambulatory BPs and nocturnal BP reduction. Symmetric AASI was not superior to standard AASI. In sensitivity analysis, 24-h AASI was a better predictor of cardiovascular outcomes in women, in younger individuals, and in nondiabetic individuals.
AASI is a predictor of cardiovascular morbidity and mortality in resistant hypertension, over and beyond traditional risk factors and other ambulatory BP monitoring parameters.
The activation of the members of the myocyte enhancer factor-2 family (MEF2A, B, C and D) of transcription factors promotes cardiac hypertrophy and failure. However, the role of its individual ...components in the pathogenesis of cardiac hypertrophy remains unclear.
In this study, we investigated whether MEF2C plays a role in mediating the left ventricular hypertrophy by pressure overload in mice. The knockdown of myocardial MEF2C induced by specific small interfering RNA (siRNA) has been shown to attenuate hypertrophy, interstitial fibrosis and the rise of ANP levels in aortic banded mice. We detected that the depletion of MEF2C also results in lowered levels of both PGC-1alpha and mitochondrial DNA in the overloaded left ventricle, associated with enhanced AMP:ATP ratio. Additionally, MEF2C depletion was accompanied by defective activation of S6K in response to pressure overload. Treatment with the amino acid leucine stimulated S6K and suppressed the attenuation of left ventricular hypertrophy and fibrosis in the aforementioned aortic banded mice.
These findings represent new evidences that MEF2C depletion attenuates the hypertrophic responses to mechanical stress and highlight the potential of MEF2C to be a target for new therapies to cardiac hypertrophy and failure.
MMP‐2 regulates rat ventral prostate development in vitro Bruni‐Cardoso, Alexandre; Rosa‐Ribeiro, Rafaela; Pascoal, Vinicius D. B. ...
Developmental dynamics,
March 2010, 2010-Mar, 2010-03-00, 20100301, Volume:
239, Issue:
3
Journal Article
In this work, we present a novel strategy for correcting imperfections in occupancy grid maps called map decay. The objective of map decay is to correct invalid occupancy probabilities of map cells ...that are unobservable by sensors. The strategy was inspired by an analogy between the memory architecture believed to exist in the human brain and the maps maintained by an autonomous vehicle. It consists in merging sensory information obtained during runtime (online) with a priori data from a high-precision map constructed offline. In map decay, cells observed by sensors are updated using traditional occupancy grid mapping techniques and unobserved cells are adjusted so that their occupancy probabilities tend to the values found in the offline map. This strategy is grounded in the idea that the most precise information available about an unobservable cell is the value found in the high-precision offline map. Map decay was successfully tested and is still in use in the IARA autonomous vehicle from Universidade Federal do Espírito Santo.
We propose a bio-inspired foveated technique to detect cars in a long range camera view using a deep convolutional neural network (DCNN) for the IARA self-driving car. The DCNN receives as input (i) ...an image, which is captured by a camera installed on IARA's roof; and (ii) crops of the image, which are centered in the waypoints computed by IARA's path planner and whose sizes increase with the distance from IARA. We employ an overlap filter to discard detections of the same car in different crops of the same image based on the percentage of overlap of detections' bounding boxes. We evaluated the performance of the proposed augmented-range vehicle detection system (ARVDS) using the hardware and software infrastructure available in the IARA self-driving car. Using IARA, we captured thousands of images of real traffic situations containing cars in a long range. Experimental results show that ARVDS increases the Average Precision (AP) of long range car detection from 29.51% (using a single whole image) to 63.15%.
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