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

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

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

Avionics-based GNSS integrity augmentation for unmanned aerial systems sense-and-avoid

This paper investigates the synergies between a GNSS Avionics Based Integrity Augmentation (ABIA) system and a novel Unmanned Aerial System (UAS) Sense-and-Avoid (SAA) architecture for cooperative and non-cooperative scenarios. The integration of ABIA with SAA has the potential to provide an integrity-augmented SAA solution that will allow the safe and unrestricted access of UAS to commercial airspace. The candidate SAA system uses Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) for the cooperative case. In the non-cooperative scenario, the system employs navigation-based image stabilization with image morphology operations and a multi-branch Viterbi filter for obstacle detection, which allows heading estimation. The system utilizes a Track-to-Track (T3) algorithm for data fusion that allows combining data from different tracks obtained with FLS and/or ADS-B depending on the scenario. Successively, it utilizes an Interacting Multiple Model (IMM) algorithm to estimate the state vector allowing a prediction of the intruder trajectory over a specified time horizon. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuver is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole SAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Various simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented SAA (IAS) architecture. The selected host platform was the AEROSONDE Unmanned Aerial Vehicle (UAV) and the simulation cases addressed a variety of cooperative and non-cooperative scenarios in a representative cross-section of the AEROSONDE operational flight envelope. The simulation results show that the proposed IAS architecture is an excellent candidate to perform high-integrity Collision Detection and Resolution (CD&R) utilizing GNSS as the primary source of navigation data, providing solid foundation for future research and developments in this domain

Avionics-based GNSS integrity augmentation for unmanned aerial systems sense-and-avoid

This paper investigates the synergies between a GNSS Avionics Based Integrity Augmentation (ABIA) system and a novel Unmanned Aerial System (UAS) Sense-and-Avoid (SAA) architecture for cooperative and non-cooperative scenarios. The integration of ABIA with SAA has the potential to provide an integrity-augmented SAA solution that will allow the safe and unrestricted access of UAS to commercial airspace. The candidate SAA system uses Forward-Looking Sensors (FLS) for the non-cooperative case and Automatic Dependent Surveillance-Broadcast (ADS-B) for the cooperative case. In the non-cooperative scenario, the system employs navigation-based image stabilization with image morphology operations and a multi-branch Viterbi filter for obstacle detection, which allows heading estimation. The system utilizes a Track-to-Track (T3) algorithm for data fusion that allows combining data from different tracks obtained with FLS and/or ADS-B depending on the scenario. Successively, it utilizes an Interacting Multiple Model (IMM) algorithm to estimate the state vector allowing a prediction of the intruder trajectory over a specified time horizon. Both in the cooperative and non-cooperative cases, the risk of collision is evaluated by setting a threshold on the Probability Density Function (PDF) of a Near Mid-Air Collision (NMAC) event over the separation area. So, if the specified threshold is exceeded, an avoidance manoeuver is performed based on a heading-based Differential Geometry (DG) algorithm and optimized utilizing a cost function with minimum time constraints and fuel penalty criteria weighted as a function of separation distance. Additionally, the optimised avoidance trajectory considers the constraints imposed by the ABIA in terms of GNSS constellation satellite elevation angles, preventing degradation or losses of navigation data during the whole SAA loop. This integration scheme allows real-time trajectory corrections to re-establish the Required Navigation Performance (RNP) when actual GNSS accuracy degradations and/or data losses take place (e.g., due to aircraft-satellite relative geometry, GNSS receiver tracking, interference, jamming or other external factors). Various simulation case studies were accomplished to evaluate the performance of this Integrity-Augmented SAA (IAS) architecture. The selected host platform was the AEROSONDE Unmanned Aerial Vehicle (UAV) and the simulation cases addressed a variety of cooperative and non-cooperative scenarios in a representative cross-section of the AEROSONDE operational flight envelope. The simulation results show that the proposed IAS architecture is an excellent candidate to perform high-integrity Collision Detection and Resolution (CD&R) utilizing GNSS as the primary source of navigation data, providing solid foundation for future research and developments in this domain

Detect-and-Avoid: Flight Test 6 Scripted Encounters Data Analysis

The Unmanned Aircraft System (UAS) in the National Airspace System (NAS) project conducted Flight Test 6 (FT6) in 2019. The ultimate goal of this flight test was to produce data to inform RTCA SC-228's Phase II Minimum Operational Performance Standards (MOPS) for Detect and Avoid (DAA) and Low Size, Weight, and Power Sensors. This report documents the analysis of scripted encounters' data. Scripted encounters own were analyzed and categorized based on the outcome of alert, maneuver guidance, and effectiveness of pilots' maneuver in resolving conflicts. Results indicate that UAS pilots' decisions as well as intruder maneuvers are leading factors that contribute to ineffective DAA maneuvers. Results also show that adding buffers to the DAA's suggested minimum turn angle improves effectiveness of the DAA maneuvers

Air traffic control surveillance accuracy and update rate study

The results of an air traffic control surveillance accuracy and update rate study are presented. The objective of the study was to establish quantitative relationships between the surveillance accuracies, update rates, and the communication load associated with the tactical control of aircraft for conflict resolution. The relationships are established for typical types of aircraft, phases of flight, and types of airspace. Specific cases are analyzed to determine the surveillance accuracies and update rates required to prevent two aircraft from approaching each other too closely

Evaluating GNSS integrity augmentation techniques for UAS sense-and-avoid

Global Navigation Satellite Systems (GNSS) far exceed the navigation accuracies provided by other state-of-the-art sensors for aerospace applications. This can support the development of low-cost and high performance navigation and guidance architectures for Unmanned Aircraft Systems (UAS) and, in conjunction with suitable data link technologies, the provision of Automated Dependent Surveillance (ADS) functionalities for cooperative Sense-and-Avoid (SAA). In non-cooperative SAA, the adoption of GNSS can also provide the key positioning and, in some cases, attitude data (using multiple antennas) required for automated collision avoidance. A key limitation of GNSS for both cooperative (ADS) and non-cooperative applications is represented by the achievable levels of integrity. Therefore, an Avionics Based Integrity Augmentation (ABIA) solution is proposed to support the development of an integrity-augmented SAA architecture suitable for both cooperative and non-cooperative scenarios. The performance of this Integrity-Augmented SAA (IAS) architecture was evaluated in representative simulation case studies. Additionally, the ABIA performances in terms of False Alarm Rate (FAR) and Detection Probability (DP) were assessed and compared with Space-Based and Ground-Based Augmentation Systems (SBAS/GBAS). Simulation results show that the proposed IAS architecture is capable of performing high-integrity conflict detection and resolution when GNSS is used as the primary source of navigation data and there is a synergy with SBAS/GBAS in providing suitable (predictive and reactive) integrity flags in all flight phases. Therefore, the integration of ABIA with SBAS/GBAS is a clear opportunity for future research towards the development of a Space-Ground-Avionics Augmentation Network (SGAAN) for UAS SAA and other safety-critical aviation applications

Stuck in Traffic (SiT) Attacks: A Framework for Identifying Stealthy Attacks that Cause Traffic Congestion

Recent advances in wireless technologies have enabled many new applications in Intelligent Transportation Systems (ITS) such as collision avoidance, cooperative driving, congestion avoidance, and traffic optimization. Due to the vulnerable nature of wireless communication against interference and intentional jamming, ITS face new challenges to ensure the reliability and the safety of the overall system. In this paper, we expose a class of stealthy attacks -- Stuck in Traffic (SiT) attacks -- that aim to cause congestion by exploiting how drivers make decisions based on smart traffic signs. An attacker mounting a SiT attack solves a Markov Decision Process problem to find optimal/suboptimal attack policies in which he/she interferes with a well-chosen subset of signals that are based on the state of the system. We apply Approximate Policy Iteration (API) algorithms to derive potent attack policies. We evaluate their performance on a number of systems and compare them to other attack policies including random, myopic and DoS attack policies. The generated policies, albeit suboptimal, are shown to significantly outperform other attack policies as they maximize the expected cumulative reward from the standpoint of the attacker