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1. Towards Energy-Aware Feedback Planning for Long-Range Autonomous Underwater Vehicles

   https://doi.org/10.3389/frobt.2021.621820  

   Alam, Tauhidul ; Al Redwan Newaz, Abdullah ; Bobadilla, Leonardo ; Alsabban, Wesam H. ; Smith, Ryan N. ; Karimoddini, Ali ( March 2021 , Frontiers in Robotics and AI)

   null (Ed.)

   Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new generation of long-range autonomous underwater vehicles (LRAUVs), such as the Slocum glider and Tethys-class AUV, has emerged with high endurance, long-range, and energy-aware capabilities. These new vehicles provide an effective solution to study different oceanic phenomena across multiple spatial and temporal scales. For these vehicles, the ocean environment has forces and moments from changing water currents which are generally on the order of magnitude of the operational vehicle velocity. Therefore, it is not practical to generate a simple trajectory from an initial location to a goal location in an uncertain ocean, as the vehicle can deviate significantly from the prescribed trajectory due to disturbances resulted from water currents. Since state estimation remains challenging in underwater conditions, feedback planning must incorporate state uncertainty that can be framed into a stochastic energy-aware path planning problem. This article presents an energy-aware feedback planning method for an LRAUV utilizing its kinematic model in an underwater environment under motion and sensor uncertainties. Our method uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. Energy-aware feedback plans for different water current layers are synthesized through sampling and ocean dynamics. The synthesized feedback plans provide strategies for the vehicle that drive it from an environment’s initial location toward the goal location. We validate our method through extensive simulations involving the Tethys vehicle’s kinematic model and incorporating actual ocean model prediction data. 

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2. Joint Link-level and Network-level Reconfiguration for mmWave Backhaul Survivability in Urban Environments

   https://doi.org/10.1145/3345768.3355913  

   Liu, Yuchen ; Hu, Qiang ; Blough, Douglas M. ( January 2019 , Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems)

   mmWave communication has been recognized as a highly promising technology for 5G wireless backhaul, which is capable of providing multi-gigabit per second transmission rates. However, in urban wireless backhaul environments, unforeseen events can cause short-term blockages or node failures and, therefore, network survivability is extremely important. In this paper, we investigate a novel relay-assisted mmWave backhaul network architecture, where a number of small-cell BSs and relays are deployed, e.g. on the lampposts of urban streets. Relays are used to provide multi-hop line-of-sight paths between small-cell BSs, which form logical links of the network. In this scenario, the interconnected logical links make up a mesh network, which offers opportunities for both link-level and network-level reconfiguration. We propose two joint link-network level reconfiguration schemes for recovery after exceptional events. One prioritizes relay path (link-level) reconfiguration and uses alternate network-level paths only if necessary. The other splits traffic on both reconfigured links and backup paths to improve network throughput. Simulation results demonstrate that the proposed schemes significantly outperform purely link-level and purely network-level reconfiguration schemes. The proposed approaches are shown to not only maintain high network throughput but to also provide robust blockage/fault tolerance across a range of scenarios for urban mmWave backhaul networks. 

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3. Context‐aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems

   https://doi.org/10.1049/itr2.12327  

   Valiente, Rodolfo ; Raftari, Arash ; Mahjoub, Hossein Nourkhiz ; Razzaghpour, Mahdi ; Mahmud, Syed K. ; Fallah, Yaser P. ( January 2023 , IET Intelligent Transport Systems)

   Vehicle‐to‐Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non‐line‐of‐sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors' failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, a Context‐Aware Target Classification (CA‐TC) module coupled with a hybrid learning‐based predictive modeling technique for CVS systems is proposed. The CA‐TC consists of two modules: a Context‐Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA‐TC, making them more robust and reliable. The CAM leverages vehicles' path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real‐world data, a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior is learned. Offline training and online model updates are combined with on‐the‐fly forecasting to account for new possible driver behaviors. Finally, the framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

    

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4. Energy-Aware Resource Management in Vehicular Edge Computing Systems

   https://doi.org/10.1109/IC2E48712.2020.00012  

   Bahreini, Tayebeh ; Brocanelli, Marco ; Grosu, Daniel ( April 2020 , Proc. of the IEEE International Conference on Cloud Engineering (IC2E 2020))

   The low-latency requirements of connected electric vehicles and their increasing computing needs have led to the necessity to move computational nodes from the cloud data centers to edge nodes such as road-side units (RSU). However, offloading the workload of all the vehicles to RSUs may not scale well to an increasing number of vehicles and workloads. To solve this problem, computing nodes can be installed directly on the smart vehicles, so that each vehicle can execute the heavy workload locally, thus forming a vehicular edge computing system. On the other hand, these computational nodes may drain a considerable amount of energy in electric vehicles. It is therefore important to manage the resources of connected electric vehicles to minimize their energy consumption. In this paper, we propose an algorithm that manages the computing nodes of connected electric vehicles for minimized energy consumption. The algorithm achieves energy savings for connected electric vehicles by exploiting the discrete settings of computational power for various performance levels. We evaluate the proposed algorithm and show that it considerably reduces the vehicles' computational energy consumption compared to state-of-the-art baselines. Specifically, our algorithm achieves 15-85% energy savings compared to a baseline that executes workload locally and an average of 51% energy savings compared to a baseline that offloads vehicles' workloads only to RSUs. 

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5. Cooperative Driving of Connected Autonomous Vehicles using Responsibility Sensitive Safety Rules: A Control Barrier Functions Approach

   MOHAMMAD KHAYATIAN, VECNA ROBOTICS MOHAMMADREZA ( June 2024 , ACM transactions on cyberphysical systems)

   Connected Autonomous Vehicles (CAVs) are expected to enable reliable, efficient, and intelligent transportation systems. Most motion planning algorithms for multi-agent systems implicitly assume that all vehicles/agents will execute the expected plan with a small error and evaluate their safety constraints based on this fact. This assumption, however, is hard to keep for CAVs since they may have to change their plan (e.g., to yield to another vehicle) or are forced to stop (e.g., A CAV may break down). While it is desired that a CAV never gets involved in an accident, it may be hit by other vehicles and sometimes, preventing the accident is impossible (e.g., getting hit from behind while waiting behind the red light). Responsibility-Sensitive Safety (RSS) is a set of safety rules that defines the objective of CAV to blame, instead of safety. Thus, instead of developing a CAV algorithm that will avoid any accident, it ensures that the ego vehicle will not be blamed for any accident it is a part of. Original RSS rules, however, are hard to evaluate for merge, intersection, and unstructured road scenarios, plus RSS rules do not prevent deadlock situations among vehicles. In this paper, we propose a new formulation for RSS rules that can be applied to any driving scenario. We integrate the proposed RSS rules with the CAV’s motion planning algorithm to enable cooperative driving of CAVs. We use Control Barrier Functions to enforce safety constraints and compute the energy optimal trajectory for the ego CAV. Finally, to ensure liveness, our approach detects and resolves deadlocks in a decentralized manner. We have conducted different experiments to verify that the ego CAV does not cause an accident no matter when other CAVs slow down or stop. We also showcase our deadlock detection and resolution mechanism using our simulator. Finally, we compare the average velocity and fuel consumption of vehicles when they drive autonomously with the case that they are autonomous and connected. 

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