Formation Control of Connected Vehicles

Liu, Di

doi:10.33612/diss.214749289

Cited by 6 publications

(6 citation statements)

References 126 publications

(312 reference statements)

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“…During learning the optimal parameters, we considered various optimization algorithms, such as the genetic algorithm [3], grid search method, etc. As in ref.…”

Section: Implementation Details and Compared Methodsmentioning

confidence: 99%

“…The field of autonomous driving has gained significant attention worldwide, leading to extensive research efforts in recent years [1,2]. The autonomous driving system consists of several components, including environmental perception, decision-making and planning, and control execution [3]. A critical aspect of a safe autonomous driving system is accurate vehicle tracking, which enables the ego-car system to plan an appropriate path and avoid collisions with other vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Sun

Liu

2023

Electronics

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Vehicle pose estimation with LIDAR plays a crucial role in autonomous driving systems. It serves as the fundamental basis for functions such as tracking, path planning, and decision-making. However, the majority of current vehicle pose estimation techniques struggle to produce satisfactory results when faced with incomplete observation measurements, such as L-shaped point cloud clusters without side contours or those including side-view mirrors. In addition, the requirement for real-time results further increases the difficulty of the pose estimation task. In this paper, we present a vehicle Pose Estimation method with Heuristic L-shape fitting and grid-based Particle Filter (PE-HL-PF). We design a geometric shape classifier module to divide clusters into symmetrical and asymmetrical ones according to their shape features. Furthermore, a contour-based heuristic L-shape fitting module is introduced for asymmetrical clusters, and a structure-aware grid-based particle filter is used to estimate the pose of symmetrical clusters. PE-HL-PF first utilizes a heuristic asymmetrical module that selects dominant contours fitting orientation in a heuristic manner, thereby avoiding the need for a complex traversal search. Additionally, a symmetrical module based on particle filtering is incorporated to enhance the stability of orientation estimation. This method achieves significant improvements in both the runtime efficiency and pose estimation accuracy of incomplete point clouds. Compared with state-of-the-art pose estimation methods, our PE-HL-PF demonstrates a notable performance improvement. Our method can estimate the pose of thousands of objects in less than 1 millisecond, a significant improvement over previous methods. The results of experiments performed on the KITTI dataset validate the effectiveness of our approach.

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“…During learning the optimal parameters, we considered various optimization algorithms, such as the genetic algorithm [3], grid search method, etc. As in ref.…”

Section: Implementation Details and Compared Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Sun

Liu

2023

Electronics

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“…Hence, as compared to other surveys which are mostly divided based on the navigation algorithm used (e.g., reinforcement learning, model predictive control, etc.) [14][15][16][17], this paper is more concerned with the situations that these algorithms can handle. Rather than focusing on the methodology or the type of algorithm used, this article provides a more comprehensive approach to deal with the considered scenarios and identifies overall shortcomings that should be addressed for each scenario.…”

Section: Selecting Edge Casesmentioning

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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“…Another emerging field in adaptive routing models is the development of connected automated vehicles (CAV) technology [27,28], vehicle-to-vehicle (V2V) commnuication [29], and automonous vehicle tracking [30,31], with a variation to consider user preferences [32]. CAVs can provide real-time information about the individual position and velocity of vehicles, which can be used to develop more accurate and efficient routing algorithms.…”

Section: Related Workmentioning

confidence: 99%

Non-Aggressive Adaptive Routing in Traffic

Prasad,

Dimitrov,

Nikolova

2023

Mathematics

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Routing a person through a traffic road network presents a tension between selecting a fixed route that is easy to navigate and selecting an aggressively adaptive route that minimizes travel time. In this paper, we propose a novel routing framework that strikes a balance between adaptability and simplicity. Specifically, we propose to create non-aggressive adaptive routes that seek the best of both these extremes in the navigation world. These selected routes still adapt to changing traffic conditions, but we limit the number of adjustments made en route. This framework improves the driver experience by providing a continuum of options between saving travel time and reducing navigation stress. We design strategies to model single and multiple route adjustments, and investigate numerous techniques to solve these models for better route selection. To alleviate the intractability of handling real-life traffic data, we devise efficient algorithms with easily computable lower and upper bounds. We finally perform computational experiments on our algorithms to demonstrate the benefits of limited adaptability in terms of reducing the travel time.

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Formation Control of Connected Vehicles

Abstract: Liu, D. (2022). Formation Control of Connected Vehicles: from cooperative to mixed humandriven/automated platoons. [Thesis fully internal (DIV),

Cited by 6 publications

References 126 publications

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

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

Non-Aggressive Adaptive Routing in Traffic

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