A Switched Control Strategy of Heterogeneous Vehicle Platoon for Multiple Objectives With State Constraints

Zhai, Chunjie; Liu, Yonggui; Luo, Fei

doi:10.1109/tits.2018.2841980

Cited by 85 publications

(56 citation statements)

References 39 publications

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“…The fuel-efficiency of a vehicular platoon was maximized by optimizing the speed and motion trajectories of the autonomous vehicles with the aid of a particle swarm optimization algorithm. In [25], a switched control strategy of a heterogeneous vehicular platoon was proposed for improving the safety, passenger comfort, formation control and fuel economy of intelligent vehicles.…”

Section: A State-of-the-artmentioning

confidence: 99%

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

Liu

Yue

et al. 2020

IEEE Trans. Veh. Technol.

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A novel framework is proposed for enhancing the driving safety and fuel economy of autonomous vehicles (AVs) with the aid of vehicle-to-infrastructure (V2I) communication networks. The problem of driving trajectory design is formulated for minimizing the total fuel consumption, while enhancing driving safety (by obeying the traffic rules and avoiding obstacles). In an effort to solve this pertinent problem, a deep reinforcement learning (DRL) approach is proposed for making collision-free decisions. Firstly, a deep Q-network (DQN) aided algorithm is proposed for determining the trajectory and velocity of the AV by receiving real-time traffic information from the base stations (BSs). More particularly, the AV acts as an agent to carry out optimal action such as lane change and velocity change by interacting with the environment. Secondly, to overcome the large overestimation of action values by the Q-learning model, a double deep Q-network (DDQN) algorithm is proposed by decomposing the max-Q-value operation into action selection and action evaluation. Additionally, three practical driving policies are also proposed as benchmarks. Numerical results are provided for demonstrating that the proposed trajectory design algorithms are capable of enhancing the driving safety and fuel economy of AVs. We demonstrate that the proposed DDQN based algorithm outperforms the DQN based algorithm. Additionally, it is also demonstrated that the proposed fuel-economy (FE) based driving policy derived from the DRL algorithm is capable of achieving in excess of 24% of fuel savings over the benchmarks.

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Section: A State-of-the-artmentioning

confidence: 99%

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

Liu

Yue

et al. 2020

IEEE Trans. Veh. Technol.

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“…Because of the advantages of MPC in achieving multiobjective optimization [27][28][29], the MPC is applied into the multiobjective optimization of the ACC system.…”

Section: Model Predictive Control (Mpc) For Multiple Objectivesmentioning

confidence: 99%

Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle

Zhang

Zhuan

2019

Mathematical Problems in Engineering

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This paper studies the control strategy for adaptive cruise control (ACC) system on a battery electric vehicle (BEV) in the car-following process, and the highlight of this paper is that the regeneration braking of BEV is considered in the car-following process. The hierarchical control structure is adopted for the ACC system. And the structure contains an upper controller and a lower controller. In the upper controller, multiple objectives including the safety, tracking, comfort, and energy consumption are optimized by using the model predictive control (MPC) method. In the lower controller, the energy is recovered during braking. So the energy economy is improved by reducing energy consumption and increasing energy recovery. The proposed ACC strategy is evaluated in simulation experiment. In the simulation experiment, safe tracking for the front vehicle is guaranteed, and the comfort and the energy economy are improved greatly. So the proposed adaptive cruise control strategy can make ACC more widely used in BEVs.

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“…Because of the advantages of MPC in achieving multi-objective optimization [29][30][31], the MPC is applied to the multi-objective optimization of the ACC system. In addition, considering the different objectives in Equations (21), (22) and (27) and the reference trajectory in Equation (26), the discretized objective function based on MPC theory can be obtained in symmetrical form:…”

Section: Multi-objective Optimization Algorithmmentioning

confidence: 99%

Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle Considering Weight Adjustment

Zhang

Zhuan

2019

Symmetry

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This paper studies control strategies for adaptive cruise control (ACC) systems in battery electric vehicles (BEVs). A hierarchical control structure is adopted for the ACC system, and the structure contains an upper controller and a lower controller. This paper focuses on the upper controller. In the upper controller, model predictive control (MPC) is applied for optimizing multiple objectives in the car-following process. In addition, multiple objectives, including safety, tracking, comfort, and energy economy, can be transformed into a symmetric objective function with constraints in MPC. In the objective function, the corresponding weight matrix for the optimization of multiple objectives is implemented in symmetric form to reduce the computational complexity. The weights in the weight matrix are usually set to be constant. However, the motion states of the own vehicle and the front vehicle change with respect to time during a car-following process, resulting in variation of the driving conditions. MPCs with constant weights do not adapt well to changes in driving conditions, which limits the performance of the ACC system. Therefore, a strategy for weight adjustment is proposed in order to improve the tracking performance, in which some weights in MPC can be adjusted according to the relative velocity of two vehicles in real time. The simulation experiments are carried out to demonstrate the effectiveness of the strategy for weight adjustment. Based on achieving the other control objectives, the ACC system with the weight adjustment has better tracking performance than the ACC system with the constant weight. While the tracking is improved, the energy economy is also improved.

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A Switched Control Strategy of Heterogeneous Vehicle Platoon for Multiple Objectives With State Constraints

Cited by 85 publications

References 39 publications

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach

Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle

Study on Adaptive Cruise Control Strategy for Battery Electric Vehicle Considering Weight Adjustment

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