251 research outputs found

    Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks

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    Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters

    Distributed Model Predictive Control for Heterogeneous Vehicle Platoon with Inter-Vehicular Spacing Constraints

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    This paper proposes a distributed control scheme for a platoon of heterogeneous vehicles based on the mechanism of model predictive control (MPC). The platoon composes of a group of vehicles interacting with each other via inter-vehicular spacing constraints, to avoid collision and reduce communication latency, and aims to make multiple vehicles driving on the same lane safely with a close range and the same velocity. Each vehicle is subject to both state constraints and input constraints, communicates only with neighboring vehicles, and may not know a priori desired setpoint. We divide the computation of control inputs into several local optimization problems based on each vehicle’s local information. To compute the control input of each vehicle based on local information, a distributed computing method must be adopted and thus the coupled constraint is required to be decoupled. This is achieved by introducing the reference state trajectories from neighboring vehicles for each vehicle and by employing the interactive structure of computing local problems of vehicles with odd indices and even indices. It is shown that the feasibility of MPC optimization problems is achieved at all time steps based on tailored terminal inequality constraints, and the asymptotic stability of each vehicle to the desired trajectory is guaranteed even under a single iteration between vehicles at each time. Finally, a comparison simulation is conducted to demonstrate the effectiveness of the proposed distributed MPC method for heterogeneous vehicle control with respect to normal and extreme scenarios

    Resilience in Platoons of Cooperative Heterogeneous Vehicles: Self-organization Strategies and Provably-correct Design

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    This work proposes provably-correct self-organizing strategies for platoons of heterogeneous vehicles. We refer to self-organization as the capability of a platoon to autonomously homogenize to a common group behavior. We show that self-organization promotes resilience to acceleration limits and communication failures, i.e., homogenizing to a common group behavior makes the platoon recover from these causes of impairments. In the presence of acceleration limits, resilience is achieved by self-organizing to a common constrained group behavior that prevents the vehicles from hitting their acceleration limits. In the presence of communication failures, resilience is achieved by self-organizing to a common group observer to estimate the missing information. Stability of the self-organization mechanism is studied analytically, and correctness with respect to traffic actions (e.g. emergency braking, cut-in, merging) is realized through a provably-correct safety layer. Numerical validations via the platooning toolbox OpenCDA in CARLA and via the CommonRoad platform confirm improved performance through self-organization and the provably-correct safety layer

    Analyzing Attacks on Cooperative Adaptive Cruise Control (CACC)

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    Cooperative Adaptive Cruise Control (CACC) is one of the driving applications of vehicular ad-hoc networks (VANETs) and promises to bring more efficient and faster transportation through cooperative behavior between vehicles. In CACC, vehicles exchange information, which is relied on to partially automate driving; however, this reliance on cooperation requires resilience against attacks and other forms of misbehavior. In this paper, we propose a rigorous attacker model and an evaluation framework for this resilience by quantifying the attack impact, providing the necessary tools to compare controller resilience and attack effectiveness simultaneously. Although there are significant differences between the resilience of the three analyzed controllers, we show that each can be attacked effectively and easily through either jamming or data injection. Our results suggest a combination of misbehavior detection and resilient control algorithms with graceful degradation are necessary ingredients for secure and safe platoons.Comment: 8 pages (author version), 5 Figures, Accepted at 2017 IEEE Vehicular Networking Conference (VNC
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