Large-Scale Vehicle Platooning: Advances and Challenges in Scheduling and Planning Techniques

Hou, Jing; Chen, Guang; Huang, Jin; Qiao, Yingjun; Li, Xiong; Wen, Fuxi; Knoll, Alois; Jiang, Changjun

doi:10.1016/j.eng.2023.01.012

Cited by 9 publications

(5 citation statements)

References 181 publications

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“…When the AHVP is in other formations, the second vehicle and the third vehicle are in the different lanes but have the same S-axis coordinates when using the Frenet coordinate system, so the spacing is set as [dS 1,2 = 20, dS 2,3 = 0, dS 3,4 = 20]. For the CS policy, the target velocity of each following vehicle is calculated by Equation (7).…”

Section: Constant Spacing Policy Velocity Designmentioning

confidence: 99%

“…In addition, as the proportion of the platoons in the traffic increases, the unreasonable lane-changing behaviours of other vehicles will be effectively reduced, which could reduce the number of traffic accidents [5]. Therefore, more and more researchers have begun to research on the communication technology, control methods, and decision planning for AVPs, with a purpose of applying AVPs to intelligent logistics and transportation [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Research of obstacle vehicles avoidance for automated heavy vehicle platoon by switching the formation

Kuang,

Tan,

Guo

et al. 2024

IET Intelligent Trans Sys

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With the development of automated vehicles, researches related to automated vehicle platoon (AVP) have received more and more attention. AVP is considered one of the effective means to alleviate traffic congestion and reduce vehicle energy consumption. This paper studies a three‐layer method of avoiding obstacle vehicles in traffic by switching the formation for the automated heavy vehicle platoon. In the decision‐making layer, a decision‐making system based on the finite‐state machine is established for formation switching. In the second layer, the lane‐changing trajectory is optimized based on the quantic polynomial curve fitting for vehicles that need to change lanes. In terms of vehicle control layer, each vehicle has a longitudinal controller based on sliding mode control and a lateral controller based on model predictive control to track the planned trajectory to complete the target formation. Finally, the proposed method is simulated in MATLAB/TruckSim. The simulation results show that the proposed method could effectively avoid the obstacle vehicles by switching the formation and has a small average value of errors in speed tracking and trajectory tracking.

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Section: Constant Spacing Policy Velocity Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research of obstacle vehicles avoidance for automated heavy vehicle platoon by switching the formation

Kuang,

Tan,

Guo

et al. 2024

IET Intelligent Trans Sys

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show abstract

“…As highlighted in the list of CCAV applications (Table A1, Appendix A), the platooning application is becoming increasingly popular, a trend underlined by its various advantages [64]. Currently, CCAV platoons are researched with three key topologies: centralised, decentralised, and hybrid.…”

Section: Platooningmentioning

confidence: 99%

“…Here, platooning relies on cloud infrastructure, Figure 2, and its operation can be decomposed into the microservices it performs. These microservices are [64] Given that platooning microservices are vulnerable to attacks (Table A2), due to its reliance on external connectivity and developed situational awareness as described in Section 4.2, it becomes crucial to explore the threat landscape of CCAV within the context of the platooning application.…”

Section: Platooningmentioning

confidence: 99%

A Comprehensive Survey of Threats in Platooning—A Cloud-Assisted Connected and Autonomous Vehicle Application

Sheik,

Maple,

Epiphaniou

et al. 2023

Information

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Cloud-Assisted Connected and Autonomous Vehicles (CCAV) are set to revolutionise road safety, providing substantial societal and economic advantages. However, with the evolution of CCAV technology, security and privacy threats have increased. Although several studies have been published around the threat and risk estimation aspects of CCAV, limited research exists on the security implications and emerging threat landscapes in the CCAV platooning application. We conducted an extensive review and categorisation of real-world security incidents and created an account of 132 threats from scholarly sources and 64 threats from recorded events in practice. Furthermore, we defined thirty-one (31) trust domains and outlined eight (8) unique attack vectors to supplement existing research efforts for the systematic security analysis of such cyberinfrastructures. Using these findings, we create a detailed attack taxonomy to communicate threat-related information in CCAV and platooning applications and highlight emerging challenges and ways to safeguard the broader CCAV systems. This work acts as a roadmap to existing researchers and practitioners advocating for a ‘security and privacy by design’ framework for a dynamically evolving CCAV threat landscape.

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“…Active-set methods, as explored by [30], focus on efficiently solving Quadratic Programming (QP) problems through active constraint selection and updating, thereby reducing computation time, though they may require careful tuning and implementation. In [31] the benefits of parallelism in solving large-scale MPC problems, although complex synchronization and load balancing may be needed for distributed computing. Event-triggered (MPC) offers advantages such as reduced computational load, enhanced energy efficiency, lower communication overhead, and improved robustness to disturbances [32]- [34].…”

mentioning

confidence: 99%

Neural-Network Based MPC for Enhanced Lateral Stability in Electric Vehicles

Hassan,

Ruiz-Moreno,

Frejo

et al. 2024

IEEE Access

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Distributed electric drive vehicles offer maneuver-ability but face stability challenges under different driving conditions. Model Predictive Control (MPC) algorithms can improve lateral stability, but their high computational demands hinder real-time implementation. To address this, the proposed strategy combines Nonlinear Autoregressive Exogenous (NARX) neural networks with MPC in two ways, namely, Nonlinear Prediction-Nonlinear Optimization (NMPC-NO) and Nonlinear Prediction-Linearization (MPC-NPL). While NMPC-NO involves online nonlinear optimization, MPC-NPL uses local linearization, reducing both the computational load significantly to about 40% of the computation time of MPC and 0.05% of that of nonlinear model predictive control (NMPC). The neural networks are trained and validated on 20 different datasets, with alternative training methods investigated. MATLAB/Simulink simulations under various standardized tests demonstrate the effectiveness of the proposed techniques, highlighting improved handling performance, reduced computation time, and real-time deployment capabilities. Recent research underscores Direct Yaw Moment Control (DYC) as effective for improving handling stability [6]-[8]. DYC comprises two control levels, with the upper controller

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Large-Scale Vehicle Platooning: Advances and Challenges in Scheduling and Planning Techniques

Cited by 9 publications

References 181 publications

Research of obstacle vehicles avoidance for automated heavy vehicle platoon by switching the formation

Research of obstacle vehicles avoidance for automated heavy vehicle platoon by switching the formation

A Comprehensive Survey of Threats in Platooning—A Cloud-Assisted Connected and Autonomous Vehicle Application

Neural-Network Based MPC for Enhanced Lateral Stability in Electric Vehicles

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