A model to estimate and interpret the energy-efficiency of movement patterns in urban road traffic

Urban road traffic is highly dynamic. Traffic conditions vary in time and with location and so do the movement patterns of individual road users. In this article, a movement pattern is the behaviour of a car when traversing a road link in an urban road network. A movement pattern can be recorded with a global navigation satellite system (GNSS), such as the Global Positioning System (GPS). A movement pattern has a specific energy-efficiency, which is a measure of how fuel-intensively the car is moving. For example, a car driving uniformly at medium speed consumes little fuel and, therefore, is energy-efficient, whereas stop-and-go driving consumes much fuel and is energy-inefficient. In this article we introduce a model to estimate the energy-efficiency of movement patterns in urban road traffic from GNSS data. First, we derived statistical features about the car's movement along the road. Then, we compared these to fuel consumption data from the car's controller area network (CAN) bus, normalized to the car's overall range of fuel consumption. We identified the optimal feature set for prediction. With the optimal feature set we trained, tested and verified a model to estimate energy-efficiency, with the fuel consumption serving as ground truth. Existing fuel consumption models usually view movement as a snapshot. Thus, the behaviour of the car remains unknown that causes a movement pattern to be energy-efficient or energy-inefficient. Our model views movement as a process and allows to interpret this process. A movement pattern can, for example, be energy-inefficient because the car is driving in stop-and-go traffic, because it is travelling at high speed, or because it is accelerating. Our model allows to distinguish between these different types of behaviours. Thus, it can provide new insights into the dynamics of urban road traffic and its energy-efficiency.     

A general‐purpose task execution framework for manipulation mission of the 2017 Mohamed Bin Zayed International Robotics Challenge

This paper presents the hardware and software of our team's EurecarBot for Challenge 2 in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). Fully automating our robots actions in a real environment required many component technologies for manipulation and vision processing. To perform the complex robotic missions, we developed a task execution framework, which provides a high‐level interface to specify the given tasks. In this study, we focus on the valve operation problem, which was the hardest part of the competition. We also discuss how we overcame the various problems caused by differences between the experimental and the actual competition environments. EurecarBot completed the valve operation mission perfectly in the MBZIRC Grand Challenge and ranked fourth in Challenge 2 and fifth in the Grand Challenge.     

Multiple Information Feedback Control Scheme for an Improved Car‐Following Model

Based upon the classical car‐following theory, an extended car‐following model considering variable safety distance (VSD) and time‐delay effect is constructed to improve driving efficiency and safety. Considering multiple preceding cars' speed differences and the difference between headway and safety distance, a new control signal is designed from the viewpoint of multiple information feedback control strategy. Stability criterion for the modified model is acquired by applying control theory. Several numerical simulations are used to show the effects of multiple preceding cars' speed changes and variable safety distance on car‐following phenomena, and the outcomes prove that the presented model can enhance traffic safety and suppress traffic jams.     

Autonomous car driving by a humanoid robot

Enabling a humanoid robot to drive a car requires the development of a set of basic primitive actions. These include walking to the vehicle, manually controlling its commands (e.g., ignition, gas pedal, and steering) and moving with the whole body to ingress/egress the car. We present a sensor‐based reactive framework for realizing the central part of the complete task, consisting of driving the car along unknown roads. The proposed framework provides three driving strategies by which a human supervisor can teleoperate the car or give the robot full or partial control of the car. A visual servoing scheme uses features of the road image to provide the reference angle for the steering wheel to drive the car at the center of the road. Simultaneously, a Kalman filter merges optical flow and accelerometer measurements to estimate the car linear velocity and correspondingly compute the gas pedal command for driving at a desired speed. The steering wheel and gas pedal reference are sent to the robot control to achieve the driving task with the humanoid. We present results from a driving experience with a real car and the humanoid robot HRP‐2Kai. Part of the framework has been used to perform the driving task at the DARPA Robotics Challenge.     

Autonomous landing on a moving vehicle with an unmanned aerial vehicle

This paper addresses the perception, control, and trajectory planning for an aerial platform to identify and land on a moving car at 15 km/hr. The hexacopter unmanned aerial vehicle (UAV), equipped with onboard sensors and a computer, detects the car using a monocular camera and predicts the car future movement using a nonlinear motion model. While following the car, the UAV lands on its roof, and it attaches itself using magnetic legs. The proposed system is fully autonomous from takeoff to landing. Numerous field tests were conducted throughout the year‐long development and preparations for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 competition, for which the system was designed. We propose a novel control system in which a model predictive controller is used in real time to generate a reference trajectory for the UAV, which are then tracked by the nonlinear feedback controller. This combination allows to track predictions of the car motion with minimal position error. The evaluation presents three successful autonomous landings during the MBZIRC 2017, where our system achieved the fastest landing among all competing teams.     

The ETH‐MAV Team in the MBZ International Robotics Challenge

This study describes the hardware and software systems of the Micro Aerial Vehicle (MAV) platforms used by the ETH Zurich team in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The aim was to develop robust outdoor platforms with the autonomous capabilities required for the competition, by applying and integrating knowledge from various fields, including computer vision, sensor fusion, optimal control, and probabilistic robotics. This paper presents the major components and structures of the system architectures and reports on experimental findings for the MAV‐based challenges in the competition. Main highlights include securing the second place both in the individual search, pick, and place the task of Challenge 3 and the Grand Challenge, with autonomous landing executed in less than 1 min and a visual servoing success rate of over for object pickups.     

Mental load and risk in traffic behaviour

Abstract

Car driving means accomplishing a variety of continuously varying driving subtasks which constitute workload on the driver. Total workload can be analysed by type; for instance, the amount of information to be processed while driving, or the effort of car control. One of the driver's main tasks is to cope with the hazards with which he can be confronted on each particular route. This task places demands on his mental capacities. Therefore, coping with hazards is part of the total workload of car drivers. Based on this premise, the following topics are discussed in this paper.

(1)The mental-load approach in modelling traffic behaviour is described in some detail.

(2)Procedures and results of investigations into load factors in car driving are presented, emphasizing workload by reference to hazards. These include a job-analytic study of driving behaviour, a simulation study of hazard perception, and a field study of drivers' exposure to different road conditions.

(3)Hazards and risks must be perceived before making adequate decisions. Some aspects of hazard perception are therefore mentioned.

(4)To understand ‘risky decision making’ in car driving it is necessary to consider the opportunities drivers have to engage in risky situations. Those opportunities are discussed.

(5)To complement these discussions of traffic behaviour, certain issues of risk-taking behaviour in non-traffic situations are considered in order to assess whether knowledge from these areas can be applied to driving.     

A Computer Vision System for Lateral Localization

For urban driving, knowledge of ego‐vehicle's position is a critical piece of information that enables advanced driver‐assistance systems or self‐driving cars to execute safety‐related, autonomous driving maneuvers. This is because, without knowing the current location, it is very hard to autonomously execute any driving maneuvers for the future. The existing solutions for localization rely on a combination of a Global Navigation Satellite System, an inertial measurement unit, and a digital map. However, in urban driving environments, due to poor satellite geometry and disruption of radio signal reception, their longitudinal and lateral errors are too significant to be used for an autonomous system. To enhance the existing system's localization capability, this work presents an effort to develop a vision‐based lateral localization algorithm. The algorithm aims at reliably counting, with or without observations of lane‐markings, the number of road‐lanes and identifying the index of the road‐lane on the roadway upon which our vehicle happens to be driving. Tests of the proposed algorithms against intercity and interstate highway videos showed promising results in terms of counting the number of road‐lanes and the indices of the current road‐lanes.     

Cooperative autonomous search,grasping, and delivering in a treasure hunt scenario by a team of unmanned aerial vehicles

This paper addresses the problem of autonomous cooperative localization, grasping and delivering of colored ferrous objects by a team of unmanned aerial vehicles (UAVs). In the proposed scenario, a team of UAVs is required to maximize the reward by collecting colored objects and delivering them to a predefined location. This task consists of several subtasks such as cooperative coverage path planning, object detection and state estimation, UAV self‐localization, precise motion control, trajectory tracking, aerial grasping and dropping, and decentralized team coordination. The failure recovery and synchronization job manager is used to integrate all the presented subtasks together and also to decrease the vulnerability to individual subtask failures in real‐world conditions. The whole system was developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017, where it achieved the highest score and won Challenge No. 3—Treasure Hunt. This paper does not only contain results from the MBZIRC 2017 competition but it also evaluates the system performance in simulations and field tests that were conducted throughout the year‐long development and preparations for the competition.     

Firefighter learning at a distance – a longitudinal study

This article presents a summary analysis of a 5‐year study on the implementation of a technology‐supported distance firefighter training programme in Sweden, focused on the firefighter students' learning processes regarding challenges, contradictions and changes that occurred during the implementation period. With activity theory as the theoretical basis, three data collections were carried out. The analysis, based on interviews with firefighter students and instructors as well as observation logbooks and educational documents, identified two phases, an implementation phase and a dissemination phase. The implementation phase is characterized by the distance students developing a self‐directed and goal‐oriented learning, supported by the revised and technology‐supported training design. During the dissemination phase, when many technology‐inexperienced instructors become involved in the distance programme, a number of challenges and contradictions are identified, which, however, turn out to be a driving force for the students to develop alternative learning strategies. Finally, vocational distance training is discussed in terms of potential opportunities for developing vocational students' learning processes.     

Al‐Robotics team: A cooperative multi‐unmanned aerial vehicle approach for the Mohamed Bin Zayed International Robotic Challenge

The Al‐Robotics team was selected as one of the 25 finalist teams out of 143 applications received to participate in the first edition of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC), held in 2017. In particular, one of the competition Challenges offered us the opportunity to develop a cooperative approach with multiple unmanned aerial vehicles (UAVs) searching, picking up, and dropping static and moving objects. This paper presents the approach that our team Al‐Robotics followed to address that Challenge 3 of the MBZIRC. First, we overview the overall architecture of the system, with the different modules involved. Second, we describe the procedure that we followed to design the aerial platforms, as well as all their onboard components. Then, we explain the techniques that we used to develop the software functionalities of the system. Finally, we discuss our experimental results and the lessons that we learned before and during the competition. The cooperative approach was validated with fully autonomous missions in experiments previous to the actual competition. We also analyze the results that we obtained during the competition trials.     

Designing on‐demand education for simultaneous development of domain‐specific and self‐directed learning skills

On‐demand education enables individual learners to choose their learning pathways according to their own learning needs. They must use self‐directed learning (SDL) skills involving self‐assessment and task selection to determine appropriate pathways for learning. Learners who lack these skills must develop them because SDL skills are prerequisite to developing domain‐specific skills. This article describes the design of an on‐demand learning environment developed to enable novices to simultaneously develop their SDL and domain‐specific skills. Learners received advice on their self‐assessments and their selections of subsequent learning tasks. In the domain of system dynamics – a way to model a dynamic system and draw graphs depicting the system's behaviour over time – advice on self‐assessment is provided in a scoring rubric containing relevant performance standards. Advice on task selection indicates all relevant task aspects to be taken into account, including recommendations for suitable learning tasks which meet the individual learner's needs. This article discusses the design of the environment and the learners' perceptions of its usefulness. Most of the times, the learners found the advice appropriate and they followed it in 78% of their task selections.     

An Extended Car‐Following Model With Consideration of the Driver's Memory and Control Strategy

Taking account of the effect of the driver's memory, an extended car‐following model is proposed in this paper. A control signal including the velocity contrast of considered car and the following car is taken into account in this extended model. Numerical simulations are implemented to prove that the application of the model can suppress the traffic congestion successfully.     

A Review of:“The Remembering Self: Construction and Accuracy in the Self-narrative”, edited by ULRIC NEISSER and ROBYN FIVUSH,Cambridge University Press,New York (1994), pp. x + 301, £30.00, ISBN 0-521-43194-8

Driving is a task that requires the timely detection of critical events and relevant changes in traffic circumstances. Adaptation of speed and safety margins allows drivers to control the time available to react to potential hazards. One of the basic safety margins in driving is the time headway preserved with respect to cars ahead. To avoid rear-end collisions, drivers have to detect decelerations of lead cars. It can be assumed that fast or abrupt decelerations of the lead car are detected faster than slow or gradual decelerations. Moreover, expected decelerations are presumably detected faster than unexpected decelerations. Drivers' responses to rather abrupt and more gradual decelerations of the lead were investigated in a driving simulator. Situational traffic cues were used to manipulate the driver's expectations. Drivers adjusted the timing of their responses very well to the level of deceleration of the lead car. If cues in the environment indicated that the lead car was likely to decelerate, drivers reacted faster. Moreover, drivers increased their headway before the lead car actually started to decelerate, which can be considered an anticipatory response. In general, anticipation allows drivers to maintain their preferred headway and control time pressure in driving.     

Team VALOR's ESCHER: A Novel Electromechanical Biped for the DARPA Robotics Challenge

The Electric Series Compliant Humanoid for Emergency Response (ESCHER) platform represents the culmination of four years of development at Virginia Tech to produce a full‐sized force‐controlled humanoid robot capable of operating in unstructured environments. ESCHER's locomotion capability was demonstrated at the DARPA Robotics Challenge (DRC) Finals when it successfully navigated the 61 m loose dirt course. Team VALOR, a Track A team, developed ESCHER leveraging and improving upon bipedal humanoid technologies implemented in previous research efforts, specifically for traversing uneven terrain and sustained untethered operation. This paper presents the hardware platform, software, and control systems developed to field ESCHER at the DRC Finals. ESCHER's unique features include custom linear series elastic actuators in both single and dual actuator configurations and a whole‐body control framework supporting compliant locomotion across variable and shifting terrain. A high‐level software system designed using the robot operating system integrated various open‐source packages and interfaced with the existing whole‐body motion controller. The paper discusses a detailed analysis of challenges encountered during the competition, along with lessons learned that are critical for transitioning research contributions to a fielded robot. Empirical data collected before, during, and after the DRC Finals validate ESCHER's performance in fielded environments.     

Risky Social Networking Practices Among “Underage” Users: Lessons for Evidence‐Based Policy

European self‐regulation to ensure children's safety on social networking sites requires that providers ensure children are old enough to use the sites, aware of safety messages, empowered by privacy settings, discouraged from disclosing personal information, and supported by easy to use reporting mechanisms. This article assesses the regulatory framework with findings from a survey of over 25000 9‐ to 16‐year‐olds from 25 European countries. These reveal many underage children users, and many who lack the digital skills to use social networking sites safely. Despite concerns that children defy parental mediation, many comply with parental rules regarding social networking. The implications of the findings are related to policy decisions on lower age limits and self‐regulation of social networking sites.     

Coordinating multi‐level cognitive assistance and incorporating dynamic confidence information in driver‐vehicle interfaces

Automation technologies have now been widely introduced into many aspects of human–machine collaboration. Most automated systems, however, are still far from perfect in performing assistive duties for their operators. This situation can lead to chaotic assistance, which can easily cause cognitive confusion and performance degradation among operators. This article discusses how to coordinate multiple types of assistance that have different levels of cognitive intrusiveness. To improve the cognitive adaptability of operators, dynamic confidence information is presented on the interactive interface to indicate the likelihood of the correctness of such assistance. Multiple types of assistance are then presented at different moments to match human‐cognitive‐processing stages and eventually to attain a coordinated status. In a car‐following driving experiment, the drivers' responses to multiple types of assistance with dynamic confidence information were investigated. The coordinated assistance successfully enhanced the drivers' driving performance and the human–machine relationship. This method can be further generalized to improve human–machine collaboration in many industrial automation systems that still require human operators. © 2012 Wiley Periodicals, Inc.     

Haptic perceptions in the vehicle seat

The perceptual overloads of visually and auditorily based information and their interference phenomena within vehicles led to research for the applicability of haptically based information and the haptic interfaces to intelligent vehicles. Because seats are the interface that touches the largest area of the driver's body, the driver's seat in vehicles has been the focus of a promising haptic interface that can improve the safety of drivers and the effectiveness and efficiency of the information transfer between vehicles and drivers. This study aims to provide practical guidelines as a building block for designing the haptic (or vibrotactile) interface in a vehicle's driver's seat by investigating, through four experiments, 1) proper intensity of vibration, 2) minimum distance of spatially distinguishable vibrations, 3) proper position and direction of vibration, and 4) proper rhythm of vibration. Twenty participants took part in the experiments, which were conducted in driving simulation environments. These environments consisted of a real car seat, commercial vibration actuators (i.e., the eccentric motors), and a monitor that showed scenes of the road while driving. This study recommended the proper intensity (approximately 26 to 34 Hz and 2.0 to 3.4 G), position (seat pan or back support), direction (horizontal or indirect), intervibration distance (8 to 9 cm), and rhythm of vibration (3‐s duration with 0.5‐s interval), and showed how the characteristics of drivers, such as gender and age, had effects on setting the design variables of the haptic interface in the vehicle seat. © 2010 Wiley Periodicals, Inc.     

基于指纹认证的行车安全系统设计

汽车应用的普及带来汽车安全的隐患,基于指纹认证的行车安全系统可实现对驾驶员信息认证、汽车行驶过程、行驶状况、道路状况和交通信息的实时监控,有效预防汽车行驶的安全问题。详细阐述行车安全系统的功能、系统模块设计、硬件系统设计和软件系统设计,分析汽车终端模块的硬件结构和软件功能实现方法,给出基于VC＋＋的汽车终端功能实现的数据类。通过测试,系统能实时、准确地获取汽车行驶的动态数据,有效实现汽车安全管理。     

An Active Disturbance Rejection Approach to Leader‐Follower Controlled Formation

This article describes the design of a linearizing, observer‐based, robust dynamic feedback control scheme for output reference trajectory tracking tasks in a leader‐follower non‐holonomic car formation problem. The approach is based on the cars' kinematic models. A radical simplification in the form of a global ultra‐model is proposed on the follower's exact open loop position tracking error dynamics obtained via flatness considerations. This results in a system described by an additively disturbed set of two, second order integrators with non‐linear velocity dependent control input gain matrix. The unknown additive disturbances are modeled as absolutely uniformly bounded time signals which may be locally approximated by arbitrary elements of a sufficiently high degree family of Taylor polynomials. Linear high‐gain Luenberger observers of the generalized proportional integral (GPI) type may be readily designed. These observers include the self updating internal model of the unknown disturbance input vector components in the form of generic, instantaneous, time‐polynomial models. The proposed (GPI) observers, which are the dual counterpart of GPI controllers [17], achieve a simultaneous disturbance estimation and tracking error phase variables estimation. This on‐line gathered information is used to advantage on the follower's feedback controller thus allowing for a simple, yet efficient, disturbance and control input gain cancelation effort. The results are applied to have the follower track a time‐delayed version of the actual leader's trajectory. Experimental results are presented which illustrate the robustness and viability of the proposed approach.