Abstract
This paper investigates cooperative adaptive cruise control (CACC) for mixed platoons consisting of both human-driven vehicles (HVs) and automated vehicles (AVs). This research is critical because the penetration rate of AVs in the transportation system will remain unsaturated for a long time. Uncertainties and randomness are prevalent in human driving behaviours and highly affect the platoon safety and stability, which need to be considered in the CACC design. A further challenge is the difficulty to know the exact models of the HVs and the exact powertrain parameters of both AVs and HVs. To address these challenges, this paper proposes a data-driven model predictive control (MPC) that does not need the exact models of HVs or powertrain parameters. The MPC design adopts the technique of data-driven reachability to predict the future trajectory of the mixed platoon within a given horizon based on noisy vehicle measurements. Compared to the classic adaptive cruise control (ACC) and existing data-driven adaptive dynamic programming (ADP), the proposed MPC ensures satisfaction of constraints such as acceleration limit and safe inter-vehicular gap. With this salient feature, the proposed MPC has provably guarantee in establishing a safe and robustly stable mixed platoon despite of the velocity changes of the leading vehicle. The efficacy and advantage of the proposed MPC are verified through comparison with the classic ACC and data-driven ADP methods on both small and large mixed platoons.
Highlights
C OOPERATIVE adaptive cruise control (CACC), which leverages vehicle-to-vehicle (V2V) wireless communications, ensures a convoy of vehicles travel at the same longitudinal velocity with safe vehicular gaps
To address the above challenges, this paper aims to develop a data-driven model predictive control (MPC) for mixed platoons with unknown humandriven vehicles (HVs) model parameters, unknown propulsion time delays and measurement noise
The above background motivates this work and the main contributions are summarized as follows: 1) A data-driven robust MPC is proposed to control the ego automated vehicles (AVs) in the mixed vehicle platoon with unknown HV models
Summary
C OOPERATIVE adaptive cruise control (CACC), which leverages vehicle-to-vehicle (V2V) wireless communications, ensures a convoy of vehicles travel at the same longitudinal velocity with safe vehicular gaps. All the above control designs need to know the parameters of the OV model, which is too restrictive as the HV behaviours are difficult to be modelled exactly [10] Both the OV model of HVs and the point-mass model of AVs adopted in the above works do not include the effect of time delays in propulsion, which could affect the platoon stability. Adaptive dynamic programming (ADP) [25] has been adopted by [26]–[28] to design data-driven optimal CACC for AVs in the mixed platoon, where the HV model parameters are not required. The above background motivates this work and the main contributions are summarized as follows: 1) A data-driven robust MPC is proposed to control the ego AVs in the mixed vehicle platoon with unknown HV models. I[a,b] denotes the set of integers from a to b. 0 is a zero matrix whose dimensions are known from the context unless it is necessary to be given. s.t. is short for subject to
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