Generation of Driving Scenario Trajectories with Generative Adversarial Networks

Δημητρίου, Ανδρέας; Allsvag, Henrik; Rahrovani, Sadegh; Chehreghani, Morteza Haghir

doi:10.1109/itsc45102.2020.9294362

Cited by 13 publications

(2 citation statements)

References 11 publications

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“…This work is an extension of our publication in [7]. The extension includes different aspects such as (i) further elaboration of the methods on trajectory generation using GANs, (ii) a clustering method consistent with the proposed deep learning framework, in particular with the respective latent representation, (iii) an outlier detection mechanism of the trajectories based on the latent space representation using the developed recurrent autoencoder, (iv) discussion on the applicability of the proposed clustering and outlier detection mechanisms for Autonomous Driving applications, and (v) novel experimental studies and investigations, in particular for the clustering and outlier detection components.…”

Section: Introductionmentioning

confidence: 85%

A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

et al. 2023

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We propose a unified deep learning framework for the generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. To model and generate scenarios of trajectories with different lengths, we develop two approaches. First, we adapt the Recurrent Conditional Generative Adversarial Networks (RC-GAN) by conditioning on the length of the trajectories. This provides us the flexibility to generate variable-length driving trajectories, a desirable feature for scenario test case generation in the verification of autonomous driving. Second, we develop an architecture based on Recurrent Autoencoder with GANs to obviate the variable length issue, wherein we train a GAN to learn/generate the latent representations of original trajectories. In this approach, we train an integrated feed-forward neural network to estimate the length of the trajectories to be able to bring them back from the latent space representation. In addition to trajectory generation, we employ the trained autoencoder as a feature extractor, for the purpose of clustering and anomaly detection, to obtain further insights into the collected scenario dataset. We experimentally investigate the performance of the proposed framework on real-world scenario trajectories obtained from in-field data collection.

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Section: Introductionmentioning

confidence: 85%

A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

et al. 2023

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“…Hence, data-oriented analysis has been emphasized [17]. Artificial Intelligence (AI) has emerged as a replacement for human experts for deriving unbiased accident scenarios based on data [22]. Virdi et al [23] used deep recurrent neural networks such as Long Short-Term Memory (LSTM) algorithm to analyze the sequential pattern of accidents.…”

Section: Introductionmentioning

confidence: 99%

Vision Transformer for Detecting Critical Situations and Extracting Functional Scenario for Automated Vehicle Safety Assessment

et al. 2022

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Automated Vehicles (AVs) are attracting attention as a safer mobility option thanks to the recent advancement of various sensing technologies that realize a much quicker Perception–Reaction Time than Human-Driven Vehicles (HVs). However, AVs are not entirely free from the risk of accidents, and we currently lack a systematic and reliable method to improve AV safety functions. The manual composition of accident scenarios does not scale. Simulation-based methods do not fully cover the peculiar AV accident patterns that can occur in the real world. Artificial Intelligence (AI) techniques are employed to identify the moments of accidents from ego-vehicle videos. However, most AI-based approaches fall short in accounting for the probable causes of the accidents. Neither of these AI-driven methods offer details for authoring accident scenarios used for AV safety testing. In this paper, we present a customized Vision Transformer (named ViT-TA) that accurately classifies the critical situations around traffic accidents and automatically points out the objects as probable causes based on an Attention map. Using 24,740 frames from Dashcam Accident Dataset (DAD) as training data, ViT-TA detected critical moments at Time-To-Collision (TTC) ≤ 1 s with 34.92 higher accuracy than the state-of-the-art approach. ViT-TA’s Attention map highlighting the critical objects helped us understand how the situations unfold to put the hypothetical ego vehicles with AV functions at risk. Based on the ViT-TA-assisted interpretation, we systematized the composition of Functional scenarios conceptualized by the PEGASUS project for describing a high-level plan to improve AVs’ capability of evading critical situations. We propose a novel framework for automatically deriving Logical and Concrete scenarios specified with 6-Layer situational variables defined by the PEGASUS project. We believe our work is vital towards systematically generating highly reliable and trustworthy safety improvement plans for AVs in a scalable manner.

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Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

Xu,

Omitaomu,

Sabri

et al. 2024

Urban Info

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The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

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Generation of Driving Scenario Trajectories with Generative Adversarial Networks

Cited by 13 publications

References 11 publications

A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

Vision Transformer for Detecting Critical Situations and Extracting Functional Scenario for Automated Vehicle Safety Assessment

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

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