Prediction of Traffic Movement for Autonomous Vehicles

Biswas, Swagatam; Bianchini, Monica; Shaw, Rabindra Nath; Ghosh, Ankush

doi:10.1007/978-981-16-0598-7_12

Cited by 29 publications

(4 citation statements)

References 23 publications

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“…The architecture has been extended and adapted in many ways, such as the introduction of "bottleneck" layers to make the model's complex computations less difficult, and ResNeXt, which improves performance by introducing a new type of connectivity between residual blocks. Overall, ResNet models prove to be a robust resource in the realm of computer vision tasks, thanks to their adaptability and ability to scale, rendering them well suited for a broad range of intricate applications [7]. Convolutional neural networks (CNNs) have gained significant prominence in the field of image processing due to their adeptness at efficiently extracting crucial visual information [8].…”

Section: Background and Motivation-gan And Resnet Models: State Of Th...mentioning

confidence: 99%

“…Figure 6 exemplifies how the suggested color-detecting approach works in practice. The focus is on a pivot pixel and the values of nearby pixels are contrasted to the gradient value, which is calculated using Equation (7). If the neighboring pixel value of the pivot element is equal to or greater than the gradient, the source value of the pixel is changed to one.…”

Section: Proposed Color Detection Modelmentioning

confidence: 99%

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IFGAN—A Novel Image Fusion Model to Fuse 3D Point Cloud Sensory Data

Ignatious,

El-Sayed,

Bouktif

2024

JSAN

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To enhance the level of autonomy in driving, it is crucial to ensure optimal execution of critical maneuvers in all situations. However, numerous accidents involving autonomous vehicles (AVs) developed by major automobile manufacturers in recent years have been attributed to poor decision making caused by insufficient perception of environmental information. AVs employ diverse sensors in today’s technology-driven settings to gather this information. However, due to technical and natural factors, the data collected by these sensors may be incomplete or ambiguous, leading to misinterpretation by AVs and resulting in fatal accidents. Furthermore, environmental information obtained from multiple sources in the vehicular environment often exhibits multimodal characteristics. To address this limitation, effective preprocessing of raw sensory data becomes essential, involving two crucial tasks: data cleaning and data fusion. In this context, we propose a comprehensive data fusion engine that categorizes various sensory data formats and appropriately merges them to enhance accuracy. Specifically, we suggest a general framework to combine audio, visual, and textual data, building upon our previous research on an innovative hybrid image fusion model that fused multispectral image data. However, this previous model faced challenges when fusing 3D point cloud data and handling large volumes of sensory data. To overcome these challenges, our study introduces a novel image fusion model called Image Fusion Generative Adversarial Network (IFGAN), which incorporates a multi-scale attention mechanism into both the generator and discriminator of a Generative Adversarial Network (GAN). The primary objective of image fusion is to merge complementary data from various perspectives of the same scene to enhance the clarity and detail of the final image. The multi-scale attention mechanism serves two purposes: the first, capturing comprehensive spatial information to enable the generator to focus on foreground and background target information in the sensory data, and the second, constraining the discriminator to concentrate on attention regions rather than the entire input image. Furthermore, the proposed model integrates the color information retention concept from the previously proposed image fusion model. Furthermore, we propose simple and efficient models for extracting salient image features. We evaluate the proposed models using various standard metrics and compare them with existing popular models. The results demonstrate that our proposed image fusion model outperforms the other models in terms of performance.

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Section: Background and Motivation-gan And Resnet Models: State Of Th...mentioning

confidence: 99%

Section: Proposed Color Detection Modelmentioning

confidence: 99%

IFGAN—A Novel Image Fusion Model to Fuse 3D Point Cloud Sensory Data

Ignatious,

El-Sayed,

Bouktif

2024

JSAN

View full text Add to dashboard Cite

show abstract

“…Schreier et al perform criticality assessment using a manoeuvre-based trajectory prediction utilising a Bayesian network where each manoeuvre is modelled separately [59]. Several other approaches towards trajectory prediction exist, using data-driven machine learning techniques (e.g., [60], [61], [62], [63]).…”

Section: ) Scene Predictionmentioning

confidence: 99%

Operational Design Domain-Driven Coverage for the Safety Argumentation of Automated Vehicles

et al. 2023

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Automated Vehicles aim to increase road safety as automated driving systems (ADS) take over the human driving task in the operational design domain (ODD), introducing severe challenges for safety validation. Pure driving over many kilometers to gather enough evidence for a safety argument is not feasible. Scenario-based testing is an approach to overcome this, but challenges like parameter discretization still prevail, hindering safety assurance. This work proposes contributions towards a traceable and efficient safety argumentation for ADS built upon ODD coverage. First, ODD coverage is thoroughly quantified across all scenario levels, assuming distribution functions' availability for the scenario parameters. Secondly, a sampling method for n-dimensional scenario parameter distributions is proposed. The provided algorithms adapt an initial k-means clustering using pre-defined boundary conditions requiring significantly fewer scenarios. Furthermore, a risk metric for urban intersections is presented for scenario evaluation. The risk metric consists of two parts, scene prediction of traffic participants (TPs) and risk assessment. The scene prediction uses a manoeuvre-based motion model with a data-driven approach towards trajectory prediction, increasing the validity. For the risk assessment, a probabilistic risk prediction for the TPs is performed for each scenario scene. The risk metric shows a reasonable tradeoff between sensitivity and specificity, outperforming time-to-collision. These contributions are exemplarily applied at an intersection using a simplified setup for generating TPs and ego vehicle trajectories. The results indicate that an increased safety argumentation is enabled using the proposed methods alongside a coverage process, facilitating further research.

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“…I N self-driving systems [1], traffic light recognition (TLR) is an essential part of the perception module [2], [3]. It provides the basic but vital information for the downstream modules including prediction [4], [5], decision making and motion planning [6], [7]. Therefore it is required to obtain the precise traffic light information in real-time, which is closely related to the safety and comfort of autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving

Wang¹,

Ma²,

Yang³

2022

Preprint

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Traffic light recognition, as a critical component of the perception module of self-driving vehicles, plays a vital role in the intelligent transportation systems. The prevalent deep learning based traffic light recognition methods heavily hinge on the large quantity and rich diversity of training data. However, it is quite challenging to collect data in various rare scenarios such as flashing, blackout or extreme weather, thus resulting in the imbalanced distribution of training data and consequently the degraded performance in recognizing rare classes. In this paper, we seek to improve traffic light recognition by leveraging data synthesis. Inspired by the generative adversarial networks (GANs), we propose a novel traffic light generation approach TL-GAN to synthesize the data of rare classes to improve traffic light recognition for autonomous driving. TL-GAN disentangles traffic light sequence generation into image synthesis and sequence assembling. In the image synthesis stage, our approach enables conditional generation to allow full control of the color of the generated traffic light images. In the sequence assembling stage, we design the style mixing and adaptive template to synthesize realistic and diverse traffic light sequences. Extensive experiments show that the proposed TL-GAN renders remarkable improvement over the baseline without using the generated data, leading to the state-of-the-art performance in comparison with the competing algorithms that are used for general image synthesis and data imbalance tackling.

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Prediction of Traffic Movement for Autonomous Vehicles

Cited by 29 publications

References 23 publications

IFGAN—A Novel Image Fusion Model to Fuse 3D Point Cloud Sensory Data

IFGAN—A Novel Image Fusion Model to Fuse 3D Point Cloud Sensory Data

Operational Design Domain-Driven Coverage for the Safety Argumentation of Automated Vehicles

TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving

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