9,780 research outputs found
Probabilistic Framework for Behavior Characterization of Traffic Participants Enabling Long Term Prediction
This research aims at developing new methods that predict the behaviors of the human driven traffic participants to enable safe operation of autonomous vehicles in complex traffic environments. Autonomous vehicles are expected to operate amongst human driven conventional vehicles in the traffic at least for the next few decades. For safe navigation they will need to infer the intents as well as the behaviors of the human traffic participants using extrinsically observable information, so that their trajectories can be predicted for a time horizon long enough to do a predictive risk analysis and gracefully avert any risky situation. This research approaches this challenge by recognizing that any maneuver performed by a human driver can be divided into four stages that depend on the surrounding context: intent determination, maneuver preparation, gap acceptance and maneuver execution. It builds on the hypothesis that for a given driver, the behavior not only spans across these four maneuver stages, but across multiple maneuvers. As a result, identifying the driver behavior in any of these stages can help characterize the nature of all the subsequent maneuvers that the driver is likely to perform, thus resulting in a more accurate prediction for a longer time horizon. To enable this, a novel probabilistic framework is proposed that couples the different maneuver stages of the observed traffic participant together and associates them to a driving style. To realize this framework two candidate Multiple Model Adaptive Estimation approaches were compared: Autonomous Multiple Model (AMM) and Interacting Multiple Model(IMM) filtering approach. The IMM approach proved superior to the AMM approach and was eventually validated using a trajectory extracted from a real world dataset for efficacy. The proposed framework was then implemented by extending the validated IMM approach with contextual information of the observed traffic participant. The classification of the driving style of the traffic participant (behavior characterization) was then demonstrated for two use case scenarios. The proposed contextual IMM (CIMM) framework also showed improvements in the performance of the behavior classification of the traffic participants compared to the IMM for the identified use case scenarios. This outcome warrants further exploration of this framework for different traffic scenarios. Further, it contributes towards the ongoing endeavors for safe deployment of autonomous vehicles on public roads
Proposal of a risk model for vehicular traffic: A Boltzmann-type kinetic approach
This paper deals with a Boltzmann-type kinetic model describing the interplay
between vehicle dynamics and safety aspects in vehicular traffic. Sticking to
the idea that the macroscopic characteristics of traffic flow, including the
distribution of the driving risk along a road, are ultimately generated by
one-to-one interactions among drivers, the model links the personal (i.e.,
individual) risk to the changes of speeds of single vehicles and implements a
probabilistic description of such microscopic interactions in a Boltzmann-type
collisional operator. By means of suitable statistical moments of the kinetic
distribution function, it is finally possible to recover macroscopic
relationships between the average risk and the road congestion, which show an
interesting and reasonable correlation with the well-known free and congested
phases of the flow of vehicles.Comment: 23 pages, 3 figures, Commun. Math. Sci., 201
Influence of a system “vehicle – driver – road – environment” on the energy efficiency of the vehicles with electric drive
The purpose of this paper is to present the results of an investigation as to the interconnection between main exterior factors which can influence the power consumption during the vehicle movement in the conditions of real operation. According to the results of theoretic researches, there was determined an influence of every factor on the power consumption during vehicle movement in the modes typical for Lutsk city. There was established a contribution of the factors into the total power consumption on micro and macro levels. As a result of the study it was evaluated that an influence of a driver on a power consumption is situated within 50…80 %, an influence of an air resistance is up to 10 %, an influence of a longitudinal profile and a road resistance varies within 20…35 %. According to the results of experiments, there were determined the bus driving modes in urban conditions, and according to their results, there was built an average graph of bus movement in Lutsk city. There was made a mathematic modelling of electric vehicle movement, along with that there was taken into account the most probable range of change of the exterior factors, namely vehicle acceleration, road resistance, air resistance. It was proved that while speed is growing, the influence of road resistance and of air resistance is growing up and has a parabolic character, along with that the contribution of a driver is decreasing. The contribution of the study consists in that, There were proposed the coefficients of taking into consideration the influence of exterior factors on the power consumption by the vehicle and there was built a mathematic model for their determination. These coefficients of taking into consideration the influence of exterior factors on the power consumption give a possibility to evaluate the critical influences and to make an operative decision about the minimization of power consumption as for some specific vehicles, and for an enterprise. Further researches will focus on the plotting of telemetric means of informing, in a mode of real time, of the drivers of the vehicles, of the controllers of an enterprise about the exterior influences, that will give a possibility to make the appropriative decisions instantly. Besides, the given results can be used in order to determine the level of qualification of a driver, the state of road pavement, will give a possibility to find some more rational layout of bus stops, traffic lights, to optimize the routes of vehicles movement
Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches
The growing advancements in Autonomous Vehicles (AVs) have emphasized the
critical need to prioritize the absolute safety of AV maneuvers, especially in
dynamic and unpredictable environments or situations. This objective becomes
even more challenging due to the uniqueness of every traffic
situation/condition. To cope with all these very constrained and complex
configurations, AVs must have appropriate control architectures with reliable
and real-time Risk Assessment and Management Strategies (RAMS). These targeted
RAMS must lead to reduce drastically the navigation risks. However, the lack of
safety guarantees proves, which is one of the key challenges to be addressed,
limit drastically the ambition to introduce more broadly AVs on our roads and
restrict the use of AVs to very limited use cases. Therefore, the focus and the
ambition of this paper is to survey research on autonomous vehicles while
focusing on the important topic of safety guarantee of AVs. For this purpose,
it is proposed to review research on relevant methods and concepts defining an
overall control architecture for AVs, with an emphasis on the safety assessment
and decision-making systems composing these architectures. Moreover, it is
intended through this reviewing process to highlight researches that use either
model-based methods or AI-based approaches. This is performed while emphasizing
the strengths and weaknesses of each methodology and investigating the research
that proposes a comprehensive multi-modal design that combines model-based and
AI approaches. This paper ends with discussions on the methods used to
guarantee the safety of AVs namely: safety verification techniques and the
standardization/generalization of safety frameworks
Personalized driver workload inference by learning from vehicle related measurements
Adapting in-vehicle systems (e.g. Advanced Driver Assistance Systems, In-Vehicle Information Systems) to individual drivers’ workload can enhance safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in a proper manner. Rather than developing an average model for all drivers, a Personalized Driver Workload Inference (PDWI)
system considering individual drivers’ driving characteristics is developed using machine learning techniques via easily accessed
Vehicle Related Measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual drivers’ workload is first automatically splitted into different categories according to its inherent data characteristics using Fuzzy C means clustering. Then an implicit mapping between VRMs
and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified
into different clusters, consequently driver workload can be successfully inferred. A recently collected dataset from real-world
naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating Fuzzy C-means clustering and Support Vector Machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, F1-score and prediction time. The inter-individual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness
Methodology for development of drought Severity-Duration-Frequency (SDF) Curves
Drought monitoring and early warning are essential elements impacting drought
sensitive sectors such as primary production, industrial and consumptive water users. A
quantitative estimate of the probability of occurrence and the anticipated severity of drought
is crucial for the development of mitigating strategies. The overall aim of this study is to
develop a methodology to assess drought frequency and severity and to advance the
understanding of monitoring and predicting droughts in the future. Seventy (70)
meteorological stations across Victoria, Australia were selected for analysis. To achieve the
above objective, the analysis was initially carried out to select the most applicable
meteorological drought index for Victoria. This is important because to date, no drought
indices are applied across Australia by any Commonwealth agency quantifying drought
impacts. An evaluation of existing meteorological drought indices namely, the Standardised
Precipitation Index (SPI), the Reconnaissance Drought Index (RDI) and Deciles was first
conducted to assess their suitability for the determination of drought conditions. The use of
the Standardised Precipitation Index (SPI) was shown to be satisfactory for assessing and
monitoring meteorological droughts in Australia. When applied to data, SPI was also
successful in detecting the onset and the end of historical droughts.
Temporal changes in historic rainfall variability and the trend of SPI were investigated
using non-parametric trend techniques to detect wet and dry periods across Victoria,
Australia. The first part of the analysis was carried out to determine annual rainfall trends
using Mann Kendall (MK) and Sen’s slope tests at five selected meteorological stations with
long historical records (more than 100 years), as well as a short sub-set period (1949-2011) of
the same data set. It was found that different trend results were obtained for the sub-set. For
SPI trend analysis, it was observed that, although different results were obtained showing
significant trends, SPI gave a trend direction similar to annual precipitation (downward and
upward trends). In addition, temporal trends in the rate of occurrence of drought events (i.e.
inter-arrival times) were examined. The fact that most of the stations showed negative slopes
indicated that the intervals between events were becoming shorter and the frequency of
events was temporally increasing. Based on the results obtained from the preliminary
analysis, the trend analyses were then carried out for the remaining 65 stations. The main
conclusions from these analyses are summarized as follows; 1) the trend analysis was
observed to be highly dependent on the start and end dates of analysis. It is recommended
that in the selection of time period for the drought, trend analysis should consider the length
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of available data sets. Longer data series would give more meaningful results, thus improving
the understanding of droughts impacted by climate change. 2) From the SPI and inter-arrival
drought trends, it was observed that some of the study areas in Victoria will face more
frequent dry period leading to increased drought occurrence. Information similar to this
would be very important to develop suitable strategies to mitigate the impacts of future
droughts.
The main objective of this study was the development of a methodology to assess
drought risk for each region based on a frequency analysis of the drought severity series
using the SPI index calculated over a 12-month duration. A novel concept centric on drought
severity-duration-frequency (SDF) curves was successfully derived for all the 70 stations
using an innovative threshold approach. The methodology derived using extreme value
analysis will assist in the characterization of droughts and provide useful information to
policy makers and agencies developing drought response plans. Using regionalisation
techniques such as Cluster analysis and modified Andrews curve, the study area was
separated into homogenous groups based on rainfall characteristics. In the current Victorian
application the study area was separated into six homogeneous clusters with unique
signatures. A set of mean SDF curves was developed for each cluster to identify the
frequency and severity of the risk of drought events for various return periods in each cluster.
The advantage of developing a mean SDF curve (as a signature) for each cluster is that it
assists the understanding of drought conditions for an ungauged or unknown station, the
characteristics of which fit existing cluster groups. Non-homogeneous Markov Chain
modelling was used to estimate the probability of different drought severity classes and
drought severity class predictions 1, 2 and 3 months ahead. The non-homogeneous
formulation, which considers the seasonality of precipitation, is useful for understanding the
evolution of drought events and for short-term planning. Overall, this model predicted
drought situations 1 month ahead well. However, predictions 2 and 3 months ahead should be
used with caution.
Many parts of Australia including Victoria have experienced their worst droughts on
record over the last decade. With the threat of climate change potentially further exacerbating
droughts in the years ahead, a clear understanding of the impact of droughts is vital. The
information on the probability of occurrence and the anticipated severity of drought will be
helpful for water resources managers, infrastructure planners and government policy-makers
with future infrastructure planning and with the design and building of more resilient
communities
Learning Motion Primitives Automata for Autonomous Driving Applications
Motion planning methods often rely on libraries of primitives. The selection of primitives
is then crucial for assuring feasible solutions and good performance within the motion planner.
In the literature, the library is usually designed by either learning from demonstration, relying
entirely on data, or by model-based approaches, with the advantage of exploiting the dynamical
system’s property, e.g., symmetries. In this work, we propose a method combining data with a
dynamical model to optimally select primitives. The library is designed based on primitives with
highest occurrences within the data set, while Lie group symmetries from a model are analysed
in the available data to allow for structure-exploiting primitives. We illustrate our technique in
an autonomous driving application. Primitives are identified based on data from human driving,
with the freedom to build libraries of different sizes as a parameter of choice. We also compare
the extracted library with a custom selection of primitives regarding the performance of obtained
solutions for a street layout based on a real-world scenario
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