Bifurcation of Lane Change and Control on Highway for Tractor-Semitrailer under Rainy Weather

Peng, Tao; Guan, Zhiwei; Zhang, Ronghui; Dong, Jinqiao; Li, Kening; Xu, Hang

doi:10.1155/2017/3506053

Cited by 9 publications

(5 citation statements)

References 66 publications

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“…Wheel/rail adhesion is a very complicated process that is a ected by many factors [27][28][29], including wheel/rail contact interface, locomotive velocity, and weather conditions. Although adhesion-creep characteristic curves vary under di erent working conditions [30], all adhesion-creep characteristic curves show similar change in trend characteristics; that is, each curve has an adhesion peak point, max . e le area of the adhesion peak point is the adhesion stable zone, and the right area is the idling or sliding fault zone, as shown in Figure 2.…”

Section: Wheel/rail Adhesion State Identification Model Strategymentioning

confidence: 98%

Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

Liu

et al. 2020

Journal of Advanced Transportation

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The traction performance of heavy-haul locomotive is subject to the wheel/rail adhesion states. However, it is difficult to obtain these states due to complex adhesion mechanism and changeable operation environment. According to the influence of wheel/rail adhesion utilization on locomotive control action, the wheel/rail adhesion states are divided into four types, namely normal adhesion, fault indication, minor fault, and serious fault in this work. A wheel/rail adhesion state identification method based on particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is proposed. To this end, a wheel/rail state identification model is constructed using KELM, and then the regularization coefficient and kernel parameter of KELM are optimized by using PSO to improve its accuracy. Finally, based on the actual data, the proposed method is compared with PSO support vector machines (PSO-SVM) and basic KELM, respectively, and the results are given to verify the effectiveness and feasibility of the proposed method.

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Section: Wheel/rail Adhesion State Identification Model Strategymentioning

confidence: 98%

Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

Liu

et al. 2020

Journal of Advanced Transportation

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“…A bifurcation method is proposed [9] for lane change and control in rainy weather. The objective of the research is significant especially in increasing the safety level of huge vehicles like trucks and lorries when driving on a rainy day.…”

Section: Rainy Weathermentioning

confidence: 99%

“…Another lane marker classification mechanism has been proposed in [9]. This work has focused on the steady steering on the highways in different weather conditions including the rainy days.…”

Section: Introductionmentioning

confidence: 99%

Advances in lane marking detection algorithms for all-weather conditions

Ghani

Besar

Sani

et al. 2021

IJECE

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Driving vehicles in all-weather conditions is challenging as the lane markers tend to be unclear to the drivers for detecting the lanes. Moreover, the vehicles will move slower hence increasing the road traffic congestion which causes difficulties in detecting the lane markers especially for advanced driving assistance systems (ADAS). Therefore, this paper conducts a thorough review on vision-based lane marking detection algorithms developed for all-weather conditions. The review methodology consists of two major areas, which are a review on the general system models employed in the lane marking detection algorithms and a review on the types of weather conditions considered for the algorithms. Throughout the review process, it is observed that the lane marking detection algorithms in literature have mostly considered weather conditions such as fog, rain, haze and snow. A new contour-angle method has also been proposed for lane marker detection. Most of the research work focus on lane detection, but the classification of the types of lane markers remains a significant research gap that is worth to be addressed for ADAS and intelligent transport systems.

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“…Preview methods were previously used to minimize lateral trajectory tracking errors for traditional vehicles, including single-point and multi-point previews (Macadam, 1980). For self-driving vehicles, Emirler et al (2014) designed a double integral action controller by PID to eliminate the lateral offset on the curvature (Peng et al, 2017). Its simple structure was easily implemented and demonstrated its acceptable tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Lane-change path planning and control method for self-driving articulated trucks

Peng

Liu

Fang

et al. 2020

JICV

Self Cite

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Purpose This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety. Design/methodology/approach The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method. Findings The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system. Originality/value This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

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Bifurcation of Lane Change and Control on Highway for Tractor-Semitrailer under Rainy Weather

Cited by 9 publications

References 66 publications

Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

Advances in lane marking detection algorithms for all-weather conditions

Lane-change path planning and control method for self-driving articulated trucks

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