Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

Hurl, Braden; Czarnecki, Krzysztof; Waslander, Steven L.

doi:10.48550/arxiv.1905.00160

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This will first accomplished by resuming and extending the data acquisition campaign in real conditions that were interrupted by the COVID-19 pandemic. Alternative approaches based on acquiring synthetic data using simulators will also be tested, as they provide a relatively cost-effective way to acquire additional data that could be used to boost the generalization capacity of our trained classifiers [55][56][57]. Finally, additional unobtrusive sensor-monitoring physiological modalities such as the HR or RR [47] will be considered in future studies.…”

Section: Discussionmentioning

confidence: 99%

Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car

Doniec

Piaseczna

et al. 2022

Electronics

View full text Add to dashboard Cite

Driving a car is an activity that became necessary for exploration, even when living in the present world. Research exploring the topic of safety on the roads has therefore become increasingly relevant. In this paper, we propose a recognition algorithm based on physiological signals acquired from JINS MEME ES_R smart glasses (electrooculography, acceleration and angular velocity) to classify four commonly encountered road types: city road, highway, housing estate and undeveloped area. Data from 30 drivers were acquired in real driving conditions. Hand-crafted statistical features were extracted from the physiological signals to train and evaluate a random forest classifier. We achieved an overall accuracy, precision, recall and F1 score of 87.64%, 86.30%, 88.12% and 87.08% on the test dataset, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car

Doniec

Piaseczna

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…Griffiths and Boehm [172] create a purely virtual LiDAR only dataset. In addition, Dosovitskiy et al [173] develop an open-source simulator that can simulate multiple sensors in autonomous driving and Hurl et al [174] release a large scale, virtual, multi-modal dataset with LiDAR data and visual camera. Despite many available virtual datasets, it is an open question to which extend a simulator can represent real-world phenomena.…”

Section: More Network Architecturesmentioning

confidence: 99%

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

Feng

Haase-Schütz

Rosenbaum

et al. 2021

IEEE Trans. Intell. Transport. Syst.

897

338

View full text Add to dashboard Cite

Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of "what to fuse", "when to fuse", and "how to fuse" remain open. This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving. To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research. We then summarize the fusion methodologies and discuss challenges and open questions. In the appendix, we provide tables that summarize topics and methods. We also provide an interactive online platform to navigate each reference: https://boschresearch.github.io/multimodalperception/. 0.99 0.8 0.98 0.99 0.96 0.96 0.94 Vehicle Person Road sign Traffic light LiDAR Points Map Radar Points RGB Image

show abstract

“…The Grand Theft Auto V (GTA V) video-game was selected as the source of the data, due to the realistic lifelike scenarios and the availability of native functions that return in-game object information. Precise Synthetic Image and LiDAR (PreSIL) [20] was used as the dataset generator since it provides improvements over the native ray-casting functions and object representations [20]. Furthermore, this generator acquires a dataset with KITTIlike format, thus, the data can be directly processed by existent toolboxes, such as OpenPCDet.…”

Section: Synthetic Dataset Generationmentioning

confidence: 99%

3D Object Detection for Self-Driving Vehicles Enhanced by Object Velocity

Alexandrino,

Olyaei,

Albuquerque

et al. 2024

IEEE Access

View full text Add to dashboard Cite

A large number of vision sensors has been proposed for enabling self-driving vehicles to perceive their surroundings. Among them, Light Detection And Ranging (LiDAR) presents the unique advantage of acquiring a high resolution 3D representation of the vehicle surroundings, in the form of point clouds, which enables accurate 3D object detection. The success of the first (and current) generation LiDARs has motivated the development of a second generation of this sensor, now based on coherent detection. Second generation LiDARs thus enable not only estimating radial distance, but also radial velocity for each point of the point cloud. The objective of this work is to investigate which benefits can be obtained by considering such an additional information -radial velocity -in 3D object detection. Results show that considering object velocity is particularly helpful in objects represented by a small number of points.

show abstract

Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

Cited by 4 publications

References 0 publications

Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car

Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

3D Object Detection for Self-Driving Vehicles Enhanced by Object Velocity

Contact Info

Product

Resources

About