We present a scalable distributed target tracking algorithm based on the alternating direction method of multipliers that is well-suited for a fleet of autonomous cars communicating over a vehicle-to-vehicle network. Each sensing vehicle communicates with its neighbors to execute iterations of a Kalman filter-like update such that each agent's estimate approximates the centralized maximum a posteriori estimate without requiring the communication of measurements. We show that our method outperforms the Consensus Kalman Filter in recovering the centralized estimate given a fixed communication bandwidth. We also demonstrate the algorithm in a high fidelity urban driving simulator (CARLA), in which 50 autonomous cars connected on a time-varying communication network track the positions and velocities of 50 target vehicles using on-board cameras.
Shared Automated Vehicles (SAVs) Fleets companies are starting pilot projects nationwide. In 2020 in Fairfax Virginia it was announced the first Shared Autonomous Vehicle Fleet pilot project in Virginia. SAVs promise to improve quality of life. However, SAVs will also induce some negative externalities by generating excessive vehicle miles traveled (VMT), which leads to more congestions, energy consumption, and emissions. The excessive VMT are primarily generated via empty relocation process. Reinforcement Learning based algorithms are being researched as a possible solution to solve some of t…
ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data
Mercedes-Benz AG, Research \& Development, Sindelfingen, Germany Institute for Intelligent Systems, Esslingen University of Applied Sciences, Esslingen, Germany
A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets
Institute for Data, Systems, and Society, Massachusetts Institute of Technology（麻省理工学院数据、系统与社会研究所）；Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology（麻省理工学院电气工程与计算机科学系）；Department of Civil and Environmental Engineering, Massachusetts Institute of Technology（麻省理工学院土木工程与环境工程系）
Royal Holloway, University of London（伦敦大学皇家霍洛威学院）；Université de Limoges（利摩日大学）；Edinburgh Napier University（爱丁堡纳皮尔大学）；Université de Bordeaux（波尔多大学）
Inter-connected objects, either via public or private networks are the near future of modern societies. Such inter-connected objects are referred to as Internet-of-Things (IoT) and/or Cyber-Physical Systems (CPS). One example of such a system is based on Unmanned Aerial Vehicles (UAVs). The fleet of such vehicles are prophesied to take on multiple roles involving mundane to high-sensitive, such as, prompt pizza or shopping deliveries to your homes to battlefield deployment for reconnaissance and combat missions. Drones, as we refer to UAVs in this paper, either can operate individually (solo m…
Laboratory of Information and Decision Systems, Massachusetts Institute of Technology（麻省理工学院信息与决策系统实验室）
In autonomous Mobility on Demand (MOD) systems, customers request rides from a fleet of shared vehicles that can be automatically positioned in response to customer demand. Recent approaches to MOD systems have focused on environments where customers can only request rides through an app or by waiting at a station. This paper develops MOD fleet management approaches for ride hailing, where customers may instead request rides simply by hailing a passing vehicle, an approach of particular importance for campus MOD systems. The challenge for ride hailing is that customer demand is not explicitly …
Distributed Control Barrier Functions for Safe Multi-Vehicle Navigation in Heterogeneous USV Fleets
Massachusetts Institute of Technology（麻省理工学院）；MIT Lincoln Laboratory（MIT林肯实验室）；Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）；Charles Stark Draper Laboratory（查尔斯·斯塔克·德拉珀实验室）；Brigham Young University
University of Michigan（密歇根大学）；Argonne National Laboratory（阿贡国家实验室）；George Mason University（乔治梅森大学）
Shared Mobility-on-Demand using automated vehicles can reduce energy consumption and cost for future mobility. However, its full potential in energy saving has not been fully explored. An algorithm to minimize fleet fuel consumption while satisfying customers travel time constraints is developed in this paper. Numerical simulations with realistic travel demand and route choice are performed, showing that if fuel consumption is not considered, the MOD service can increase fleet fuel consumption due to increased empty vehicle mileage. With fuel consumption as part of the cost function, we can re…
Routing and Scheduling Optimization for Urban Air Mobility Fleet Management using Quantum Annealing
Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究科）；Sigma-i Co., Ltd.（Sigma-i 公司）；Sumitomo Corporation（住友商事）；Department of Physics, Tokyo Institute of Technology（东京工业大学物理系）
