Partially Visible Lane Detection with Hierarchical Supervision Approach

Rana, Hukam Singh; Singh, Thipendra P.; Kumar, Kurichi; Kumar, Krishan

doi:10.1080/03772063.2022.2069602

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…For this, it is important for the system to predict maneuvers of the ego vehicle as well as surrounding vehicles. By this, an AD function is enabled to align its reactions with the intended behavior of the driver [44], [79], to remind the driver of actions that need to be taken [77], to warn him or her in case the intended maneuver cannot be executed safely [43] or just keep the driver informed about the environment [84]. Examples for maneuvers whose detection and classification is desired apart from the road following maneuver are turning maneuvers [43], [45], [46], [77], [79], lane change maneuvers [43], [44], [46]- [53], [55], [64], [75], [77], [79], [85], braking [50], and drifts into another lane [54].…”

Section: B Maneuver Detection Prediction and Classificationmentioning

confidence: 99%

Literature Review on Maneuver-Based Scenario Description for Automated Driving Simulations

Neis,

Beyerer

2023

2023 IEEE Intelligent Vehicles Symposium (IV)

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The increasing complexity of automated driving functions and their growing operational design domains imply more demanding requirements on their validation. Classical methods such as field tests or formal analyses are not sufficient anymore and need to be complemented by simulations. For simulations, the standard approach is scenario-based testing, as opposed to distance-based testing primarily performed in field tests. Currently, the time evolution of specific scenarios is mainly described using trajectories, which limit or at least hamper generalizations towards variations. As an alternative, maneuverbased approaches have been proposed. We shed light on the state of the art and available foundations for this new method through a literature review of early and recent works related to maneuver-based scenario description. It includes related modeling approaches originally developed for other applications. Current limitations and research gaps are identified.Index Terms-automated driving, AI, maneuver-based, simulation, scenario-based testing *This publication was written in the context of the AVEAS research project (www.aveas.org), funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) within the program "New Vehicle and System Technologies".

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Section: B Maneuver Detection Prediction and Classificationmentioning

confidence: 99%

Literature Review on Maneuver-Based Scenario Description for Automated Driving Simulations

Neis,

Beyerer

2023

2023 IEEE Intelligent Vehicles Symposium (IV)

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Driving into the Future: A Comparative Analysis of the Hough Transform and Convolutional Neural Network for Autonomous Vehicles

Gupta,

Beniwal,

Kalra

et al. 2023

2023 9th International Conference on Signal Processing and Communication (ICSC)

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GMG-NCDVAE: Guided de novo Molecule Generation using NLP Techniques and Constrained Diverse Variational Autoencoder

Kumar

2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Text processing techniques in Natural Language Processing (NLP) find applications in many industries such as pharmaceutical, automation, and automotive. Drug design using variational autoencoders is a popular data-assisted technique to design drug molecules with control over molecular properties. It generates continuous latent space, which can be optimized. This paper introduces a constrained variational autoencoder-based molecular generation structure using the SMILES format. The proposal is accompanied by the generation of molecules, filtering them based on scores, and subsequently determining the optimal molecules by using NLP matured techniques. To generate more meaningful latent space, a condition vector of molecular properties is combined with the SMILES representation of molecules. A tunable parameter (diversity,D) is also used to control the diversity in the generated molecules. The proposed architecture is evaluated using standard datasets. Validity, uniqueness, and FCD are evaluation matrices used to access the performance of model. The validity of proposed model is maximum (92.11%) at diversity level 1. As diversity level increases the validity of generated molecules decreases. This is intuitively consistent because increased diversity reduces replicas and improves variety in the generated molecules. Thus proposed model provide control over diversity of generated molecules. The results clearly indicate that the proposed method outperforms other SMILE based methods and gives a new direction for the generation of desired molecules.

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Partially Visible Lane Detection with Hierarchical Supervision Approach

Cited by 3 publications

References 37 publications

Literature Review on Maneuver-Based Scenario Description for Automated Driving Simulations

Literature Review on Maneuver-Based Scenario Description for Automated Driving Simulations

Driving into the Future: A Comparative Analysis of the Hough Transform and Convolutional Neural Network for Autonomous Vehicles

GMG-NCDVAE: Guided de novo Molecule Generation using NLP Techniques and Constrained Diverse Variational Autoencoder

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