A hierarchical approach for splitting truck platoons near network discontinuities

Abstract

Truck platooning has attracted substantial attention due to its pronounced benefits in saving energy and promising business model. However, one prominent challenge for the successful implementation of truck platooning is the safe and efficient interaction with surrounding traffic, especially at network discontinuities where mandatory lane changes may lead to decoupling of truck platoons. This contribution put forward an efficient method for splitting a platoon of vehicles near network merges. A model-based bi-level control strategy is proposed. A supervisory tactical strategy based on a first-order car-following model with bounded acceleration is designed to maximize the flow at merge discontinuities. The decisions taken at this level include optimal vehicle order after the merge, new equilibrium gaps of automated trucks at the merging point, and anticipation horizon that truck platoon start to act to track the new equilibrium gaps. The lower-level operational layer uses a second-order longitudinal dynamics model to compute the optimal truck accelerations so that new equilibrium gaps have been created when merging vehicles start to change lane and the transient maneuvers are efficient, safe and comfortable. The tactical decisions are derived analytically and the operational accelerations are controlled via model predictive control with guaranteed stability. Simulation experiments are provided in order to test the feasibility and demonstrate the performance of the proposed strategy.

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“…The Japanese Energy ITS project also demonstrated the benefits of truck platooning in energy savings (13). While acknowledging the potential benefits of truck platooning for the trucks, concerns have been raised over difficulties that large platoons may bring to other vehicles, especially at freeway entrance/exit sections (4,8,(14)(15)(16). Such sections are often freeway bottlenecks, that have paramount implications for the safe and efficient operations of a freeway.…”

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SMITS: Research on Smart Mobility Intelligent Traffic Signal System based on Distributed Deep Reinforcement Learning

“…controlled 50 intersections in Tehran using well-known reinforcement learning models, Actor-Critic, and the classic tile coding for function approximation. Mnih et al [37][38][39]. introduced Deep Q-Network (DQN) in the Atari Learning Environment (ALE) domain.…”