Lane-Change Model and Tracking Control for Autonomous Vehicles on Curved Highway Sections in Rainy Weather

Peng, Tao; Lili, Su; Guan, Zhiwei; Hou, Haijing; Li, Jun-kai Li; Liu, Xing-liang u; Tong, Yi-ke Tong

doi:10.1155/2020/8838878

Cited by 8 publications

(4 citation statements)

References 28 publications

(25 reference statements)

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“…Additionally, due to the nature of the FSM algorithm, complex maneuvers such as lane changes were not allowed since that would have caused the required FSM logic to exponentially grow. In [34], the authors developed an adaptive path planning model for collision avoidance and lane change maneuvers for curved sections of highways. The lane change model took into account the curvature of the road, the road friction coefficient, and the presence of other vehicles.…”

Section: Adverse Weather Conditionsmentioning

confidence: 99%

Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review

2023

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The development of autonomous vehicles (AVs) is becoming increasingly important as the need for reliable and safe transportation grows. However, in order to achieve level 5 autonomy, it is crucial that such AVs can navigate through complex and unconventional scenarios. It has been observed that currently deployed AVs, like human drivers, struggle the most in cases of adverse weather conditions, unsignalized intersections, crosswalks, roundabouts, and near-accident scenarios. This review paper provides a comprehensive overview of the various navigation methodologies used in handling these situations. The paper discusses both traditional planning methods such as graph-based approaches and emerging solutions including machine-learning based approaches and other advanced decision-making and control techniques. The benefits and drawbacks of previous studies in this area are discussed in detail and it is identified that the biggest shortcomings and challenges are benchmarking, ensuring interpretability, incorporating safety as well as road user interactions, and unrealistic simplifications such as the availability of accurate and perfect perception information. Some suggestions to tackle these challenges are also presented.

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Section: Adverse Weather Conditionsmentioning

confidence: 99%

Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review

2023

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“…It can either be quantifiable perception data or road conditions data, which will be introduced in Section 9. Peng et al [257] introduced an adaptive path planning model and tracking control method for collision avoidance and lane-changing maneuvers in rainy weather. They first use Gaussian distribution to evaluate the impact of rain on the pavement and deduce the adaptive trajectory and the followed scorebased decision making and multilevel driving mode take control while maintaining safety, comfort, and efficiency.…”

Section: Planningmentioning

confidence: 99%

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Zhang¹,

Carballo²,

Yang³

et al. 2021

Preprint

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Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. Stateof-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions like V2X technology, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. By pointing out all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey contributes a holistic overview on the obstacles and directions of ADS development in terms of adverse weather driving conditions.

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“…Factors afecting the sideslip risk on curved segments under rainy conditions mainly include road surface conditions [8], road geometry [9,10], vehicle performance [11], and rainfall [12]. Previous studies have achieved many results by extensively investigating these factors using various methods, such as crash report data [13,14], simulation experiments [15,16], and actual vehicle experiments [17]. However, crash report data cannot reveal the impact of rainfall on vehicle sideslip risk at the microlevel.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Rainfall on Vehicle Trajectory Patterns and Sideslip Risk: An Empirical Investigation

Wang,

Wong,

Zhang

et al. 2024

Journal of Advanced Transportation

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Understanding the sideslip risks of various trajectory patterns, as well as the impact of rainfall on them, is critical for improving road safety. However, the lack of precise classification indicators hampers systematic analysis of the variations in vehicle trajectory patterns. To address this, this study proposes a parameterized classification method for trajectories on curved segments, employing the radius and offset of the trajectory as the primary classification features and dividing the trajectories into nine patterns. These patterns represent variations from smaller to larger radii and inside to outside lane offsets, reflecting different driving behaviors and vehicle stability during vehicle cornering. Concurrently, the friction coefficient utilization rate is used to effectively compare vehicles’ sideslip risk under different weather conditions. Based on this, we construct a framework using computer vision technology for automatically identifying trajectory patterns and measuring sideslip risk. We conducted an empirical study on a highway-curved segment with high sideslip risk in China and collected two datasets under clear and rainy conditions for analysis. The classification results show that the proposed method can effectively classify trajectories according to nine trajectory patterns. Comparative analysis reveals that vehicle trajectories in both the inside and outside lanes are notably more affected by rainfall compared to the middle lane. Meanwhile, trucks demonstrate a higher susceptibility to rainfall than cars. In addition, the analysis of the sideslip risk for different trajectory patterns discovers several high-risk patterns. This study provides an effective approach for monitoring and analyzing the sideslip risk on curved segments, thereby contributing to the enhancement of road design and traffic safety management.

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Lane-Change Model and Tracking Control for Autonomous Vehicles on Curved Highway Sections in Rainy Weather

Cited by 8 publications

References 28 publications

Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review

Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Exploring the Impact of Rainfall on Vehicle Trajectory Patterns and Sideslip Risk: An Empirical Investigation

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