Despite advancements in vehicle security systems, over the last decade, auto-theft rates have increased, and cyber-security attacks on internet-connected and autonomous vehicles are becoming a new threat. In this paper, a deep learning model is proposed, which can identify drivers from their driving behaviors based on vehicle telematics data. The proposed Long-Short-Term-Memory (LSTM) model predicts the identity of the driver based on the individual's unique driving patterns learned from the vehicle telematics data. Given the telematics is time-series data, the problem is formulated as a time series prediction task to exploit the embedded sequential information. The performance of the proposed approach is evaluated on three naturalistic driving datasets, which gives high accuracy prediction results. The robustness of the model on noisy and anomalous data that is usually caused by sensor defects or environmental factors is also investigated. Results show that the proposed model prediction accuracy remains satisfactory and outperforms the other approaches despite the extent of anomalies and noise-induced in the data.
University of Connecticut（康涅狄格大学）；Université du Québec à Montréal（魁北克大学蒙特利尔分校）
This article describes techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset. The synthetic dataset generated has 100,000 policies that included observations about driver's claims experience together with associated classical risk variables and telematics-related variables. This work is aimed to produce a resource that can be used to advance models to assess risks for usage-based insurance. It follows a three-stage process using machine learning algorithms. The first stage is simulating values for the number of claims a…
University of Connecticut（康涅狄格大学）；Université du Québec à Montréal（魁北克大学蒙特利尔分校）
Powered with telematics technology, insurers can now capture a wide range of data, such as distance traveled, how drivers brake, accelerate or make turns, and travel frequency each day of the week, to better decode driver's behavior. Such additional information helps insurers improve risk assessments for usage-based insurance (UBI), an increasingly popular industry innovation. In this article, we explore how to integrate telematics information to better predict claims frequency. For motor insurance during a policy year, we typically observe a large proportion of drivers with zero claims, a les…
Michigan State University（密歇根州立大学）；CSAA Insurance Group（CSAA保险集团）；New York University（纽约大学）
In recent years it has become possible to collect GPS data from drivers and to incorporate this data into automobile insurance pricing for the driver. This data is continuously collected and processed nightly into metadata consisting of mileage and time summaries of each discrete trip taken, and a set of behavioral scores describing attributes of the trip (e.g, driver fatigue or driver distraction) so we examine whether it can be used to identify periods of increased risk by successfully classifying trips that occur immediately before a trip in which there was an incident leading to a claim fo…
Nova Information Management School (NOVA IMS)（新里斯本大学新信息管理学院（NOVA IMS））；Campus de Campolide, Universidade Nova de Lisboa（里斯本大学新校区坎波利德校区（ Universidade Nova de Lisboa））；Universidade Nova de Lisboa（新里斯本大学）
Driving behaviour has a great impact on road safety. A popular way of analysing driving behaviour is to move the focus to the manoeuvres as they give useful information about the driver who is performing them. In this paper, we investigate a new way of identifying manoeuvres from vehicle telematics data, through motif detection in time-series. We implement a modified version of the Extended Motif Discovery (EMD) algorithm, a classical variable-length motif detection algorithm for time-series and we applied it to the UAH-DriveSet, a publicly available naturalistic driving dataset. After a syste…
Use ADAS Data to Predict Near-Miss Events: A Group-Based Zero-Inflated Poisson Approach
Department of Decision Analytics and Operations, City University of Hong Kong（决策分析与运营系，香港城市大学）；Department of Econometrics, University of Barcelona（计量经济学系，巴塞罗那大学）；Department of Data Science, City University of Hong Kong（数据科学系，香港城市大学）；Department of Information Systems, City University of Hong Kong（信息系统系，香港城市大学）
Russian Presidential Academy of National Economy and Public Administration（俄罗斯总统国民经济与公共管理学院）；National Research University Higher School of Economics（国家研究型大学高等经济学院）；The Lebedev Physical Institute of the RAS (LPI RAS)（俄罗斯科学院列别杰夫物理研究所）；Federal Research Center "Computer Science and Control" of the RAS（俄罗斯科学院计算机科学与控制联邦研究中心）；Meta Telematics（元远程信息处理公司）
The paper introduces an approach to telematics devices data application in automotive insurance. We conduct a comparative analysis of different types of devices that collect information on vehicle utilization and driving style of its driver, describe advantages and disadvantages of these devices and indicate the most efficient from the insurer point of view. The possible formats of telematics data are described and methods of their processing to a format convenient for modelling are proposed. We also introduce an approach to classify the accidents strength. Using all the available information,…
MINES ParisTech（巴黎高等矿业学院）；PSL Research University（巴黎文理研究大学）；École des Ponts ParisTech（巴黎高科桥路学校）
In order to drive safely and efficiently on public roads, autonomous vehicles will have to understand the intentions of surrounding vehicles, and adapt their own behavior accordingly. If experienced human drivers are generally good at inferring other vehicles' motion up to a few seconds in the future, most current Advanced Driving Assistance Systems (ADAS) are unable to perform such medium-term forecasts, and are usually limited to high-likelihood situations such as emergency braking. In this article, we present a first step towards consistent trajectory prediction by introducing a long short-…
California PATH, University of California, Berkeley（加州大学伯克利分校加州合作伙伴先进运输技术项目（PATH））；Tsinghua University（清华大学）；University of California, Berkeley（加州大学伯克利分校）
As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles. This paper presents an algorithm for long-horizon trajectory prediction of surrounding vehicles using a dual long short term memory (LSTM) network, which is capable of effectively improving prediction accuracy in strongly interactive driving environments. In contrast to traditional approaches which require trajectory matching and manual feature selection, this method can autom…
University of California, Berkeley（加州大学伯克利分校）；Chalmers University of Technology（查尔姆斯理工大学）；Zenuity AB
We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space. Using the CARLA simulator, we develop a parking lot environment and collect a dataset of human parking maneuvers. We then study the impact of model complexity and feature information by comparing a multi-modal Long Short-Term Memory (LSTM) prediction model and a Convolution Neural Network LSTM (CNN-LSTM) to a physics-based Extended Kalman Filter (EKF) baseline. Our results show that 1) intent…
Predicting traffic volume in real-time can improve both traffic flow and road safety. A precise traffic volume forecast helps alert drivers to the flow of traffic along their preferred routes, preventing potential deadlock situations. Existing parametric models cannot reliably forecast traffic volume in dynamic and complex traffic conditions. Therefore, in order to evaluate and forecast the traffic volume for every given time step in a real-time manner, we develop non-linear memory-based deep neural network models. Our extensive experiments run on the Metro Interstate Traffic Volume dataset de…
How autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off. Understanding human drivers' behavior can be beneficial for autonomous vehicle decision making and planning, especially when the autonomous vehicle is surrounded by human drivers who have various driving behaviors and patterns of interaction with other vehicles. In this paper, we propose an LSTM-based trajectory prediction method for human drivers which can help the autonomous vehicle make better decisions, especially in…
Short-Term Regional Electricity Demand Forecasting in Argentina Using LSTM Networks
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Departamento de Química Teórica y Computacional（阿根廷国家科学研究与技术理事会（CONICET）、科里亚物理化学研究所（INFIQC）、科里亚国立大学、化学科学学院、理论与计算化学系）
Izmir Institute of Technology（伊兹密尔理工学院）；TTTech Auto Turkey Software（TTTech汽车土耳其软件公司）
Advanced driver assistance and automated driving systems should be capable of predicting and avoiding dangerous situations. This study proposes a method to predict potentially dangerous cut-in maneuvers happening in the ego lane. We follow a computer vision-based approach that only employs a single in-vehicle RGB camera, and we classify the target vehicle's maneuver based on the recent video frames. Our algorithm consists of a CNN-based vehicle detection and tracking step and an LSTM-based maneuver classification step. It is more computationally efficient than other vision-based methods since …
University of Bamberg（班贝格大学）；IBM Deutschland GmbH（IBM德国有限公司）；fortiss GmbH（福蒂斯有限公司）
Artificial Intelligence and Digital Twins play an integral role in driving innovation in the domain of intelligent driving. Long short-term memory (LSTM) is a leading driver in the field of lane change prediction for manoeuvre anticipation. However, the decision-making process of such models is complex and non-transparent, hence reducing the trustworthiness of the smart solution. This work presents an innovative approach and a technical implementation for explaining lane change predictions of layer normalized LSTMs using Layer-wise Relevance Propagation (LRP). The core implementation includes …
Driving behaviour is one of the primary causes of road crashes and accidents, and these can be decreased by identifying and minimizing aggressive driving behaviour. This study identifies the timesteps when a driver in different circumstances (rush, mental conflicts, reprisal) begins to drive aggressively. An observer (real or virtual) is needed to examine driving behaviour to discover aggressive driving occasions; we overcome this problem by using a smartphone's GPS sensor to detect locations and classify drivers' driving behaviour every three minutes. To detect timeseries patterns in our data…
Graf Engineering（格拉夫工程公司）；Technical University of Graz（格拉茨技术大学）；University of Salzburg（萨尔茨堡大学）
Professional race drivers are still superior to automated systems at controlling a vehicle at its dynamic limit. Gaining insight into race drivers' vehicle handling process might lead to further development in the areas of automated driving systems. We present a method to study and evaluate race drivers on a driver-in-the-loop simulator by analysing tire grip potential exploitation. Given initial data from a simulator run, two optimiser based on physical models maximise the horizontal vehicle acceleration or the tire forces, respectively. An overall performance score, a vehicle-trajectory scor…
LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction
School of Information Technology, Murdoch University（默多克大学信息技术学院）；School of Medical, Molecular and Forensic Sciences, Murdoch University（默多克大学医学、分子与法医学学院）；Western Crop Genetics Alliance, Murdoch University（默多克大学西部作物遗传学联盟）；Western Australian State Agricultural Biotechnology Centre（西澳大利亚州农业生物技术中心）；Department of Primary Industries and Regional Development（初级产业与区域发展部）
Honda Research Institute, USA（美国本田研究院）；Carnegie Mellon University（卡内基梅隆大学）
The Tactical Driver Behavior modeling problem requires understanding of driver actions in complicated urban scenarios from a rich multi modal signals including video, LiDAR and CAN bus data streams. However, the majority of deep learning research is focused either on learning the vehicle/environment state (sensor fusion) or the driver policy (from temporal data), but not both. Learning both tasks end-to-end offers the richest distillation of knowledge, but presents challenges in formulation and successful training. In this work, we propose promising first steps in this direction. Inspired by t…
University of Waterloo（滑铁卢大学）；University of Tehran（德黑兰大学）
Automatic detection of traffic accidents has a crucial effect on improving transportation, public safety, and path planning. Many lives can be saved by the consequent decrease in the time between when the accidents occur and when rescue teams are dispatched, and much travelling time can be saved by notifying drivers to select alternative routes. This problem is challenging mainly because of the rareness of accidents and spatial heterogeneity of the environment. This paper studies deep representation of loop detector data using Long-Short Term Memory (LSTM) network for automatic detection of fr…
INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis
C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems（C-DRiVeS实验室：车载系统认知驾驶研究）；Computer Science and Engineering Department - Faculty of Media Engineering and Technology - German University in Cairo（计算机科学与工程系-媒体工程与技术学院-埃及德国大学）
There is significant progress in recognizing traditional human activities from videos focusing on highly distinctive actions involving discriminative body movements, body-object and/or human-human interactions. Driver's activities are different since they are executed by the same subject with similar body parts movements, resulting in subtle changes. To address this, we propose a novel framework by exploiting the spatiotemporal attention to model the subtle changes. Our model is named Coarse Temporal Attention Network (CTA-Net), in which coarse temporal branches are introduced in a trainable g…
Cornell University（康奈尔大学）；Stanford University（斯坦福大学）；Brain Of Things Inc.（Brain Of Things公司）
Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we propose a vehicular sensor-rich platform and learning algorithms for maneuver anticipation. For this purpose we equip a car with cameras…
MetaFollower: Adaptable Personalized Autonomous Car Following
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Tongji University（同济大学）；University of Washington（华盛顿大学）
In this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the vehicle's trajectory is different from that of regular moving objects since it is affected by various latent factors including road structure, traffic rules, and driver's intention. Previous state of the art approaches use sophisticated vehicle behavior model describing these factors and derive the complex trajectory prediction algorithm, which requires a system designer to conduct intensive model optimization for practical use. Our approach is …
Vehicle Intelligence Pioneers Inc.（车辆智能先锋公司）；Qingdao Academy of Intelligent Industries（青岛智能产业研究院）；Xilinx Technology Beijing Limited（北京赛灵思科技有限公司）；Sun Yat-sen University（中山大学）；University of Waterloo（滑铁卢大学）
As a combination of various kinds of technologies, autonomous vehicles could complete a series of driving tasks by itself, such as perception, decision-making, planning, and control. Since there is no human driver to handle the emergency situation, future transportation information is significant for automated vehicles. This paper proposes different methods to forecast the time series for autonomous vehicles, which are the nearest neighborhood (NN), fuzzy coding (FC), and long short term memory (LSTM). First, the formulation and operational process for these three approaches are introduced. Th…
Accurately forecasting the future movements of surrounding vehicles is essential for safe and efficient operations of autonomous driving cars. This task is difficult because a vehicle's moving trajectory is greatly determined by its driver's intention, which is often hard to estimate. By leveraging attention mechanisms along with long short-term memory (LSTM) networks, this work learns the relation between a driver's intention and the vehicle's changing positions relative to road infrastructures, and uses it to guide the prediction. Different from other state-of-the-art solutions, our work tre…
Deep Learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather
TU Berlin（柏林工业大学）；ATB（莱布尼茨农业工程与生物经济研究所）；Leipzig University（莱比锡大学）；University of Reading（雷丁大学）；The Alan Turing Institute（阿兰·图灵研究所）；University of Potsdam（波茨坦大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their travel route, departure time and travel origin selection, which can be helpful in traffic management. Multiple models and algorithms based on time series prediction and machine learning were applied to this issue and achieved acceptable results. Recently, the availability of sufficient data and computational power, motivates us to improve the pred…
The progression of innovation and technology and ease of inter-connectivity among networks has allowed us to evolve towards one of the promising areas, the Internet of Vehicles. Nowadays, modern vehicles are connected to a range of networks, including intra-vehicle networks and external networks. However, a primary challenge in the automotive industry is to make the vehicle safe and reliable; particularly with the loopholes in the existing traditional protocols, cyber-attacks on the vehicle network are rising drastically. Practically every vehicle uses the universal Controller Area Network (CA…
University of Washington（华盛顿大学）；Tongji University（同济大学）；Monash University（莫纳什大学）
Car-following refers to a control process in which the following vehicle (FV) tries to keep a safe distance between itself and the lead vehicle (LV) by adjusting its acceleration in response to the actions of the vehicle ahead. The corresponding car-following models, which describe how one vehicle follows another vehicle in the traffic flow, form the cornerstone for microscopic traffic simulation and intelligent vehicle development. One major motivation of car-following models is to replicate human drivers' longitudinal driving trajectories. To model the long-term dependency of future actions …
Generating eye diagrams by using a circuit simulator can be very computationally intensive, especially in the presence of nonlinearities. It often involves multiple Newton-like iterations at every time step when a SPICE-like circuit simulator handles a nonlinear system in the transient regime. In this paper, we leverage machine learning methods, to be specific, the recurrent neural network (RNN), to generate black-box macromodels and achieve significant reduction of computation time. Through the proposed approach, an RNN model is first trained and then validated on a relatively short sequence …
Indraprastha Institute of Information Technology, Delhi（德里英迪拉普拉斯信息技术学院）
In this paper, we train a recurrent neural network to learn dynamics of a chaotic road environment and to project the future of the environment on an image. Future projection can be used to anticipate an unseen environment for example, in autonomous driving. Road environment is highly dynamic and complex due to the interaction among traffic participants such as vehicles and pedestrians. Even in this complex environment, a human driver is efficacious to safely drive on chaotic roads irrespective of the number of traffic participants. The proliferation of deep learning research has shown the eff…
Max-Planck-Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；ELLIS Unit Jena（耶拿欧洲学习与智能系统实验室）；Michael-Stifel-Center Jena for Data-driven and Simulation Science（耶拿米夏埃尔·施蒂费尔数据驱动与模拟科学中心）
Forecasting the state of vegetation in response to climate and weather events is a major challenge. Its implementation will prove crucial in predicting crop yield, forest damage, or more generally the impact on ecosystems services relevant for socio-economic functioning, which if absent can lead to humanitarian disasters. Vegetation status depends on weather and environmental conditions that modulate complex ecological processes taking place at several timescales. Interactions between vegetation and different environmental drivers express responses at instantaneous but also time-lagged effects…
Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning
Institute for Big Data Analytics --- Dalhousie University, Halifax - NS -- Canada [.05cm]；Department of Computer Science --- Memorial University of Newfoundland, St. John's - NL -- Canada [.05cm]；Institute of Computer Science --- Polish Academy of Sciences, Warsaw -- Poland
In essence, a neural network is an arbitrary differentiable, parametrized function. Choosing a neural network architecture for any task is as complex as searching the space of those functions. For the last few years, 'neural architecture design' has been largely synonymous with 'neural architecture search' (NAS), i.e. brute-force, large-scale search. NAS has yielded significant gains on practical tasks. However, NAS methods end up searching for a local optimum in architecture space in a small neighborhood around architectures that often go back decades, based on CNN or LSTM. In this work, we p…
