PIP-Net: Pedestrian Intention Prediction in the Wild

Accurate pedestrian intention prediction (PIP) by Autonomous Vehicles (AVs) is one of the current research challenges in this field. In this article, we introduce PIP-Net, a novel framework designed to predict pedestrian crossing intentions by AVs in real-world urban scenarios. We offer two variants of PIP-Net designed for different camera mounts and setups. Leveraging both kinematic data and spatial features from the driving scene, the proposed model employs a recurrent and temporal attention-based solution, outperforming state-of-the-art performance. To enhance the visual representation of road users and their proximity to the ego vehicle, we introduce a categorical depth feature map, combined with a local motion flow feature, providing rich insights into the scene dynamics. Additionally, we explore the impact of expanding the camera's field of view, from one to three cameras surrounding the ego vehicle, leading to enhancement in the model's contextual perception. Depending on the traffic scenario and road environment, the model excels in predicting pedestrian crossing intentions up to 4 seconds in advance which is a breakthrough in current research studies in pedestrian intention prediction. Finally, for the first time, we present the Urban-PIP dataset, a customised pedestrian intention prediction dataset, with multi-camera annotations in real-world automated driving scenarios

Development of Full Speed Range ACC with SiVIC, a virtual platform for ADAS Prototyping, test and evaluation

LIVIC-IFSTTAR develops driving assistance services in order to improve the driving safety. These systems are tested on several real prototypes equipped with sensors and perception, decision and control modules. But tests on real prototypes are not always available, effectively some hardware architectures could be too expensive to implement, scenario may lead to hazardous situations. Moreover, lots of reasons could lead to the inability to obtain both sensors and ground truth data for ADAS evaluation. However, safety applications must be tested in order to guaranty their reliability. For this task, simulation appears as a good alternative to the real prototyping and testing stages. In this context, the simulation must provide the same opportunities as reality, by providing all the necessary data to develop and to prototype different types of ADAS based on local or extended environment perception. The sensor data provided by simulation must be as noised and imperfect as those obtained with real sensors. To address this issue, the SiVIC platform has been developed; it provides a virtual road environment including realistic dynamic models of mobile entities (vehicles), realistic sensors, and sensors for ground truth. To test real embedded applications, an interconnection has been developed between SiVIC and third party applications (ie. RTMaps). In this way, the prototyped application can be directly embedded in real prototypes in order to test it in real conditions. A Full Speed Range ACC application is presented in this paper to illustrate the capabilities and the functionalities of this virtual platform

Advisory speed for Intelligent Speed Adaptation in adverse conditions

In this paper, a novel approach to compute advisory speeds to be used in an adaptive Intelligent Speed Adaptation system (ISA) is proposed. This method is designed to be embedded in the vehicles. It estimates an appropriate speed by fusing in real-time the outputs of ego sensors which detect adverse conditions with roadway characteristics transmitted by distant servers. The method presents two major novelties. First, the 85 th percentile of observed speeds (V 85 ) is estimated along a road, this speed profile is considered as a reference speed practised and practicable in ideal conditions for a lonely vehicle. In adverse conditions, this reference speed is modulated in order to account for lowered friction and lowered visibility distance (top-down approach). Second, this method allows us taking into account the potential seriousness of crashes using a generic scenario of accident. Within this scenario, the difference in speed that should be applied in adverse conditions is estimated so that global injury risk is the same as in ideal conditions

A Modelling Study to Examine Threat Assessment Algorithms Performance in Predicting Cyclist Fall Risk in Safety Critical Bicycle-Automatic Vehicle lnteractions

Falls are responsible for a large proportion of serious injuries and deaths among cyclists [1-4]. A common fall scenario is loss of balance during an emergency braking maneuver to avoid another vehicle [5-7]. Automated Vehicles (AV) have the potential to prevent these critical scenarios between bicycle and cars. However, current Threat Assessment Algorithms (TAA) used by AVs only consider collision avoidance to decide upon safe gaps and decelerations when interacting wih cyclists and do not consider bicycle specific balance-related constraints. To date, no studies have addressed this risk of falls in safety critical scenarios. Yet, given the bicycle dynamics, we hypothesized that the existing TAA may be inaccurate in predicting the threat of cyclist falls and misclassify unsafe interactions. To test this hypothesis, this study developed a simple Newtonian mechanics-based model that calculates the performance of two existing TAAs in four critical scenarios with two road conditions. Tue four scenarios are: (1) a crossing scenario and a bicycle following lead car scenario in which the car either (2) suddenly braked, (3) halted or (4) accelerated from standstill. These scenarios have been identified by bicycle-car conflict studies as common scenarios where the car driver elicits an emergency braking response of the cyclist [8-11] and are illustrated in Figure 1. The two TAAs are Time-to-Collision (TTC) and Headway (H). These TAAs are commonly used by AVs in the four critical scenarios that will be modelled. The two road conditions are a flat dry road and also a downhill wet road, which serves as a worst-case condition for loss of balance during emergency braking [12]

Towards Highly Automated Driving: Intermediate report on the HAVEit-Joint System

International audienceThis overview article describes the goals, concepts and very preliminary results of the subproject Joint System within the EU-project HAVEit. The goal of HAVEit is to develop and investigate vehicle automation beyond ADAS systems, especially highly automated driving, where the automation is doing a high percentage of the driving, while the driver is still meaningfully involved in the driving task. In HAVEit, an overarching architecture and several prototypes will be built up over time by manufacturers and suppliers. As a trail blazer, a Joint System prototype is under development by an interdisciplinary team of several European research institutes in order to investigate and demonstrate the basic principles of highly automated driving, which will then be gradually applied to vehicles closer to serial production. Starting with sensor data fusion, the Co-System part of the Joint Systems plans manoeuvres and trajectories, which are then used to control active interfaces and, taking into account the results of an online driver assessment, joined with the actions of the driver. While many aspects of this research undertaking are still under investigation, the concept, a first prototype and first results from a simulator evaluation will be sketched

Streptococcus agalactiae clones infecting humans were selected and fixed through the extensive use of tetracycline

Streptococcus agalactiae (Group B Streptococcus, GBS) is a commensal of the digestive and genitourinary tracts of humans that emerged as the leading cause of bacterial neonatal infections in Europe and North America during the 1960s. Due to the lack of epidemiological and genomic data, the reasons for this emergence are unknown. Here we show by comparative genome analysis and phylogenetic reconstruction of 229 isolates that the rise of human GBS infections corresponds to the selection and worldwide dissemination of only a few clones. The parallel expansion of the clones is preceded by the insertion of integrative and conjugative elements conferring tetracycline resistance (TcR). Thus, we propose that the use of tetracycline from 1948 onwards led in humans to the complete replacement of a diverse GBS population by only few TcR clones particularly well adapted to their host, causing the observed emergence of GBS diseases in neonates. \ua9 2014 Macmillan Publishers Limited. All rights reserved

Long-range downstream enhancers are essential for Pax6 expression

AbstractPax6 is a developmental control gene with an essential role in development of the eye, brain and pancreas. Pax6, as many other developmental regulators, depends on a substantial number of cis-regulatory elements in addition to its promoters for correct spatiotemporal and quantitative expression. Here we report on our analysis of a set of mice transgenic for a modified yeast artificial chromosome carrying the human PAX6 locus. In this 420 kb YAC a tauGFP-IRES-Neomycin reporter cassette has been inserted into the PAX6 translational start site in exon 4. The YAC has been further engineered to insert LoxP sites flanking a 35 kb long, distant downstream regulatory region (DRR) containing previously described DNaseI hypersensitive sites, to allow direct comparison between the presence or absence of this region in the same genomic context. Five independent transgenic lines were obtained that vary in the extent of downstream PAX6 locus that has integrated. Analysis of transgenic embryos carrying full-length and truncated versions of the YAC indicates the location and putative function of several novel tissue-specific enhancers. Absence of these distal regulatory elements abolishes expression in specific tissues despite the presence of more proximal enhancers with overlapping specificity, strongly suggesting interaction between these control elements. Using plasmid-based reporter transgenic analysis we provide detailed characterization of one of these enhancers in isolation. Furthermore, we show that overexpression of a short PAX6 isoform derived from an internal promoter in a multicopy YAC transgenic line results in a microphthalmia phenotype. Finally, direct comparison of a single-copy line with the floxed DRR before and after Cre-mediated deletion demonstrates unequivocally the essential role of these long-range control elements for PAX6 expression

An Integrated Driver-Vehicle-Environment (I-DVE) Model to Assess Crash Risks

A wide range of driver and vehicle models have been proposed by traffic psychologists, engineers and traffic simulation researchers to assess crash risks. However, existing approaches are often confined within a single discipline and lack concepts that formally express the complexity of interactions between the driver, vehicle and environment as well as the broader scope and the interdisciplinary nature of the driving behaviour modeling. For example, traffic psychologists have defined a driver performance model as the driver's perceptual and motor skills (capabilities), or what the driver can do. In contrast, a driver behavior model refers to what the driver actually does do while driving (Evans, 1991). A driver behaviour model is determined by an infinite and complex number of factors related to the environment, driver and vehicle but is not explicitly modeled in Evans (1991). Existing driver models lack substantive concepts that express the interactions between the Driver, Vehicle and Environment (DVE). A new Integrated Driver-Vehicle-Environment (I-DVE) model is formally presented as a set of concepts and equations representing interactions between the driver, vehicle and environment with the view to assess crash risks. The I-DVE model features realistic and measurable attributes, which ultimately influence the driving performance and associated crash risks. I-DVE model is validated in a simulation. The simulation uses empirical data related to Time To Collision (TTC), Energy Equivalent Speed (EES), injury severity and driver profile to assess crash risks. This paper (i) reviews existing driver modeling approaches and highlights the need for an integrated approach, (ii) defines a novel model capable of expressing risks associated interaction between the driver, environment and vehicle and (iii) provides directions for further research in driver behaviour modeling