3,344 research outputs found

    Vehicular Fog Computing Enabled Real-time Collision Warning via Trajectory Calibration

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    Vehicular fog computing (VFC) has been envisioned as a promising paradigm for enabling a variety of emerging intelligent transportation systems (ITS). However, due to inevitable as well as non-negligible issues in wireless communication, including transmission latency and packet loss, it is still challenging in implementing safety-critical applications, such as real-time collision warning in vehicular networks. In this paper, we present a vehicular fog computing architecture, aiming at supporting effective and real-time collision warning by offloading computation and communication overheads to distributed fog nodes. With the system architecture, we further propose a trajectory calibration based collision warning (TCCW) algorithm along with tailored communication protocols. Specifically, an application-layer vehicular-to-infrastructure (V2I) communication delay is fitted by the Stable distribution with real-world field testing data. Then, a packet loss detection mechanism is designed. Finally, TCCW calibrates real-time vehicle trajectories based on received vehicle status including GPS coordinates, velocity, acceleration, heading direction, as well as the estimation of communication delay and the detection of packet loss. For performance evaluation, we build the simulation model and implement conventional solutions including cloud-based warning and fog-based warning without calibration for comparison. Real-vehicle trajectories are extracted as the input, and the simulation results demonstrate that the effectiveness of TCCW in terms of the highest precision and recall in a wide range of scenarios

    A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

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    Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

    An intelligent anti-collision system for electric vehicles applications

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    This paper presents the initial outcomes of the ongoing research to develop an intelligent online fault monitoring and anti-collision system for electric vehicle industrial applications. This is aiming to utilise the latest development in sensors technology and multi-level of redundancy approach, to improve the safety of electric vehicle and minimize the risk of road collision. This paper is focused on the development of the anti-collision system. The system is a network of sensors utilising the near real time embedded system. Four operational conditions were considered and some activities have been taken to control the speed and steering control system of the vehicle at an imminent collision. Visual alerts using LEDs were developed to indicate vehicles or obstacles along the path of the host vehicle even at an opposite direction. The proposed system was tested indoor using offshelf mini vehicle model and further field test have been planned to ensure the operability of the system in relevant applications. Research is still undertaken to develop the online fault detection, monitoring and online recovery tolerance system using multi-level of redundancy and a hot-standby dual control unit

    A Simple and Robust Dissemination Protocol for VANETs

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    Several promising applications for Vehicular Ad-hoc Networks (VANETs) exist. For most of these applications, the communication among vehicles is envisioned to be based on the broadcasting of messages. This is due to the inherent highly mobile environment and importance of these messages to vehicles nearby. To deal with broadcast communication, dissemination protocols must be defined in such a way as to (i) prevent the so-called broadcast storm problem in dense networks and (ii) deal with disconnected networks in sparse topologies. In this paper, we present a Simple and Robust Dissemination (SRD) protocol that deals with these requirements in both sparse and dense networks. Its novelty lies in its simplicity and robustness. Simplicity is achieved by considering only two states (cluster tail and non- tail) for a vehicle. Robustness is achieved by assigning message delivery responsibility to multiple vehicles in sparse networks. Our simulation results show that SRD achieves high delivery ratio and low end-to-end delay under diverse traffic conditions

    Identification, Calculation and Warning of Horizontal Curves for Low-volume Two-lane Roadways Using Smartphone Sensors

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    Smartphones and other portable personal devices that integrate global positioning systems, Bluetooth Low Energy, and advanced computing technologies have become more accessible due to affordable prices, product innovation, and people’s desire to be connected. As more people own these devices, there are greater opportunities for data acquisition in Intelligent Transportation Systems, and for vehicle-to-infrastructure communication. Horizontal curves are a common factor in the number of observed roadway crashes. Identifying locations and geometric characteristics of the horizontal curves plays a critical role in crash prediction and prevention, and timely curve warnings save lives. However, most states in the US face a challenge to maintain detailed and highquality roadway inventory databases for low volume rural roads due to the laborintensive and time-consuming nature of collecting and maintaining the data. This thesis proposes two smartphone applications C-Finder and C-Alert, to collect two-lane road horizontal curves data (including radius, superelevation, length, etc.), collect this data for transportation agencies (providing a low-cost alternative to mobile asset data collection vehicles), and for warning drivers of sharp horizontal curves, respectively. C-Finder is capable of accurately detecting horizontal curves by exploiting an unsupervised K-means machine learning technique. Butterworth low pass filtering was applied to reduce sensor noise. Extended Kalman filtering was adopted to improve GPS accuracy. Chord method-based radius computation, and superelevation estimation were introduced to achieve accurate and robust results despite of the low-frequency GPS and noisy sensor signals obtained from the smartphone. C-Alert applies BLE technology and a head-up display (HUD) to track driver speed and compare vehicle position with curve locations in a real-time fashion. Messages can be wirelessly communicated from the smartphone to a receiving unit through BLE technology, and then displayed by HUD on the vehicle’s front windshield. The field test demonstrated that C-Finder achieves high curve identification accuracy, reasonable accuracy for calculating curve radius and superelevation compared to the previous road survey studies, and C-Alert indicates relatively high accuracy for speeding warning when approaching sharp curves

    A novel on-board Unit to accelerate the penetration of ITS services

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    In-vehicle connectivity has experienced a big expansion in recent years. Car manufacturers have mainly proposed OBU-based solutions, but these solutions do not take full advantage of the opportunities of inter-vehicle peer-to-peer communications. In this paper we introduce GRCBox, a novel architecture that allows OEM user-devices to directly communicate when located in neighboring vehicles. In this paper we also describe EYES, an application we developed to illustrate the type of novel applications that can be implemented on top of the GRCBox. EYES is an ITS overtaking assistance system that provides the driver with real-time video fed from the vehicle located in front. Finally, we evaluated the GRCbox and the EYES application and showed that, for device-to-device communication, the performance of the GRCBox architecture is comparable to an infrastructure network, introducing a negligible impact

    Ground controlled robotic assembly operations for Space Station Freedom

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    A number of dextrous robotic systems and associated positioning and transportation devices are available on Space Station Freedom (SSF) to perform assembly tasks that would otherwise need to be performed by extravehicular activity (EVA) crewmembers. The currently planned operating mode for these robotic systems during the assembly phase is teleoperation by intravehicular activity (IVA) crewmembers. While this operating mode is less hazardous and expensive than manned EVA operations, and has insignificant control loop time delays, the amount of IVA time available to support telerobotic operations is much less than the anticipated requirements. Some alternative is needed to allow the robotic systems to perform useful tasks without exhausting the available IVA resources; ground control is one such alternative. The issues associated with ground control of SSF robotic systems to alleviate onboard crew time availability constraints are investigated. Key technical issues include the effect of communication time delays, the need for safe, reliable execution of remote operations, and required modifications to the SSF ground and flight system architecture. Time delay compensation techniques such as predictive displays and world model-based force reflection are addressed and collision detection and avoidance strategies to ensure the safety of the on-orbit crew, Orbiter, and SSF are described. Although more time consuming and difficult than IVA controlled teleoperations or manned EVA, ground controlled telerobotic operations offer significant benefits during the SSF assembly phase, and should be considered in assembly planning activities

    Mobile Safety Application for Pedestrians

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    Vulnerable Road User (VRU) safety has been an important issue throughout the years as corresponding fatality numbers in traffic have been increasing each year. With the developments in connected vehicle technology, there are new and easier ways of implementing Vehicle to Everything (V2X) communication which can be utilized to provide safety and early warning benefits for VRUs. Mobile phones are one important point of interest with their sensors being increased in quantity and quality and improved in terms of accuracy. Bluetooth and extended Bluetooth technology in mobile phones has enhanced support to carry larger chunks of information to longer distances. The work we discuss in this paper is related to a mobile application that utilizes the mobile phone sensors and Bluetooth communication to implement Personal Safety Message (PSM) broadcast using the SAE J2735 standard to create a Pedestrian to Vehicle (P2V) based safety warning structure. This implementation allows the drivers to receive a warning on their mobile phones and be more careful about the pedestrian intending to cross the street. As a result, the driver has much more time to safely slow down and stop at the intersection. Most importantly, thanks to the wireless nature of Bluetooth connection and long-range mode in Bluetooth 5.0, most dangerous cases such as reduced visibility or No-Line-of-Sight (NLOS) conditions can be remedied

    Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic

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    Overtaking a cyclist on a two-lane rural road with oncoming traffic is a challenging task for any driver. Failing this task can lead to severe injuries or even death, because of the potentially high impact speed in a possible collision. To avoid a rear-end collision with the cyclist, drivers need to make a timely and accurate decision about whether to steer and overtake the cyclist, or brake and let the oncoming traffic pass first. If this decision is delayed, for instance because the driver is distracted, neither braking nor steering may eventually keep the driver from crashing—at that point, rear-ending a cyclist may be the safest alternative for the driver. Active safety systems such as forward collision warning that help drivers being alert and avoiding collisions may be enhanced with driver models to reduce activations perceived as false positive. In this study, we developed a driver model based on logistic regression using data from a test-track experiment. The model can predict the probability and confidence of drivers braking and steering while approaching a cyclist during an overtaking, and therefore this model may improve collision warning systems. In both an in-sample and out-of-sample evaluation, the model identified drivers’ intent to overtake with high accuracy (0.99 and 0.90, respectively). The model can be integrated into a warning system that leverages the deviance of the actual driver behavior from the behavior predicted by the model to allow timely warnings without compromising driver acceptance
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