Cooperative Adaptive Cruise Control for String Stable Mixed Traffic: Benchmark and Human-Centered Design

Wang

2020

IEEE Access

Vehicle safety, ride comfort, formation control and fuel economy are the main control objectives of cooperative adaptive cruise control (CACC) of vehicle strings. This paper considers the multi-objective CACC problem of a mixed vehicle string, i.e., the vehicle string is composed of CACC-, ACC-and humandriven vehicles. A new switching multi-objective receding horizon predictive control method is proposed for CACC of mixed vehicle strings. The longitudinal models of ACC-and human-driven vehicles are used to estimate the driving behaviors of the individual vehicles. Then three multi-objective predictive controllers are separately designed for different vehicle scenarios. According to different preceding vehicles, the multiobjective predictive controllers are switched online in the normal situation, and in the dangerous situation, the predictive controller is switched to the safety controller to ensure driving safety of vehicles in the string. To verify the effectiveness of the proposed control method, a mixed vehicle string consisting of six cars is used in the simulation experiment with complex traffic scenarios. INDEX TERMS Multi-objective control, model predictive control, mixed vehicle strings, switching control.

Section: Simulation and Results Discussionmentioning

confidence: 99%

“…For the vehicle i ∈ I H at sampling time k, the discretetime prediction model of the human-driven vehicle can be described by [38] x…”

Section: Mixed Vehicle Strings Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Switching Multi-Objective Receding Horizon Control for CACC of Mixed Vehicle Strings

Wang

2020

IEEE Access

“…With the information exchange among vehicles in the group by vehicle-to-anything (V2X) communication [1], CACC systems can improve traffic flow stability, throughput, driving safety and ride comfort of vehicles [2,3]. CACC technology is now shown to be one of the promising intelligent transportation system technologies [2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…This property can effectively avoid a halt or collision at the end of a platoon [10,11]. Various CACC methods have been proposed to derive the (string) stability of vehicle platoons (e.g., see [5][6][7][8][9][12][13][14][15][16][17][18] and the references therein).…”

Section: Introductionmentioning

confidence: 99%

Robust Time-Delay Feedback Control of Vehicular CACC Systems with Uncertain Dynamics

Chen

Wang

et al. 2020

Sensors

This paper proposes a new, robust time-delay cooperative adaptive cruise control (CACC) approach for vehicle platooning systems with uncertain dynamics and varying communication delay. The uncertain CACC models with perturbed parameters are used to describe the uncertain dynamics of the vehicle platooning system. By combining the constant time headway strategy and predecessor-following communication topology, a set of robust delay feedback controllers is designed for the uncertain vehicle platoon with varying communication delay. Then, the set of CACC controllers is computed by solving some linear matrix inequalities, which further establish the robust (string) stability of the uncertain platooning system with the varying communication delay. The co-simulation experiment of CarSim and Simulink with a group of a seven-car platoons and varying velocity is used to demonstrate the effectiveness of the presented method.

Data‐iteration cooperative adaptive cruise control of mixed heterogeneous vehicle platoons with unknown dynamic characteristics

Asian Journal of Control

Dong

Feng

et al. 2022

This paper considers the cooperative adaptive cruise control (CACC) problem of mixed heterogeneous vehicle platoons composed of human‐driven and CACC vehicles with unknown dynamic characteristics and an alternative to CACC for platooning of the heterogeneous vehicle platoon is presented. Adaptive dynamic programming is firstly used to learn the dynamic characteristics of acceleration of vehicles from the sampled data. Then the data‐iteration optimal CACC controller is computed to ensure that each CACC vehicle can reach a desired inter‐vehicle distance and desired common velocity with no prior knowledge of the dynamics of vehicles in the mixed platoon. Moreover, the string stability of the mixed vehicle platoon is derived by establishing some sufficient conditions on the acceleration transfer function of adjacent vehicles. Two simulation experiments of a six‐vehicle mixed platoon are used to illustrate the effectiveness of the proposed CACC method.