Control method simulation and application for autonomous vehicles

Arikan, Abdullah; Kayaduman, Abdulsamed; Polat, Suat; Şimşek, Yasin; Dikmen, İsmail Can; Bakir, H. Gokhan; Karadağ, Teoman; Abbasov, Teymuraz

doi:10.1109/idap.2018.8620918

Cited by 19 publications

(12 citation statements)

References 3 publications

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“…After four residual blocks, the merged feature with a shape of 512 × H m × W m will be the straightway input to the Spatial Pyramid Pooling (SPP) module [12] for capturing multi-scale features, where the m is the downsampling rate. In this S2D mode, we build the SPP module via multi-scale grids in three different resolutions of (h, w) ∈ { (8,16), (4,8), (2, 4)}. These grid features will be uniformly interpolated to the same original resolution, and be concatenated as a new feature map, as shown in the right side of Fig.…”

Section: Edc: Event-driven Dynamic Contextmentioning

confidence: 99%

“…T HE road safety of Intelligent Vehicles (IV) is being concurrently questioned and researched intensely along with the rapid development of self-driving systems. The general pipeline of self-driving systems can be divided into four sections including (a) the sensing module to observe the environment [1]; (b) the perception module that uses captured data from the sensors to offer high-level information like recognition results about the surrounding scenes [2]; (c) the path planning module which plans the motion of the vehicle based on the sensing and perception results [3]; and (d) the control module that commands the vehicle to make certain actions [4]. In recent decades, Intelligent Transportation Systems (ITS) have been growing at a rapid pace based on the development of intelligent perception technology, which serves as a vital and basic component of self-driving systems and the quality Fig.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Event-driven Dynamic Context for Accident Scene Segmentation

Zhang¹,

Yang²,

Stiefelhagen³

2021

Preprint

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The robustness of semantic segmentation on edge cases of traffic scene is a vital factor for the safety of intelligent transportation. However, most of the critical scenes of traffic accidents are extremely dynamic and previously unseen, which seriously harm the performance of semantic segmentation methods. In addition, the delay of the traditional camera during highspeed driving will further reduce the contextual information in the time dimension. Therefore, we propose to extract dynamic context from event-based data with a higher temporal resolution to enhance static RGB images, even for those from traffic accidents with motion blur, collisions, deformations, overturns, etc. Moreover, in order to evaluate the segmentation performance in traffic accidents, we provide a pixel-wise annotated accident dataset, namely DADA-seg, which contains a variety of critical scenarios from traffic accidents. Our experiments indicate that event-based data can provide complementary information to stabilize semantic segmentation under adverse conditions by preserving fine-grained motion of fast-moving foreground (crash objects) in accidents. Our approach achieves +8.2% performance gain on the proposed accident dataset, exceeding more than 20 state-of-the-art semantic segmentation methods. The proposal has been demonstrated to be consistently effective for models learned on multiple source databases including Cityscapes, KITTI-360, BDD, and ApolloScape.

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Section: Edc: Event-driven Dynamic Contextmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring Event-driven Dynamic Context for Accident Scene Segmentation

Zhang¹,

Yang²,

Stiefelhagen³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Third, the path planning module [35], which determines the motion and the optimal path that the vehicle has to follow in order to avoid obstacles and reach a target location, based on the outputs of the perception module. Finally, the control module [36], which commands the vehicle to execute planned actions, such as accelerating, braking and steering, among others.…”

Section: B Visual Attention For Autonomous Vehiclesmentioning

confidence: 99%

Interpretable Global-Local Dynamics for the Prediction of Eye Fixations in Autonomous Driving Scenarios

2020

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Human eye movements while driving reveal that visual attention largely depends on the context in which it occurs. Furthermore, an autonomous vehicle which performs this function would be more reliable if its outputs were understandable. Capsule Networks have been presented as a great opportunity to explore new horizons in the Computer Vision field, due to their capability to structure and relate latent information. In this paper, we present a hierarchical approach for the prediction of eye fixations in autonomous driving scenarios. Context-driven visual attention can be modeled by considering different conditions which, in turn, are represented as combinations of several spatio-temporal features. With the aim of learning these conditions, we have built an encoder-decoder network which merges visual features' information using a global-local definition of capsules. Two types of capsules are distinguished: representational capsules for features and discriminative capsules for conditions. The latter and the use of eye fixations recorded with wearable eye tracking glasses allow the model to learn both to predict contextual conditions and to estimate visual attention, by means of a multi-task loss function. Experiments show how our approach is able to express either frame-level (global) or pixel-wise (local) relationships between features and contextual conditions, allowing for interpretability while maintaining or improving the performance of black-box related systems in the literature. Indeed, our proposal offers an improvement of 29% in terms of information gain with respect to the best performance reported in the literature. INDEX TERMS Top-down visual attention, eye fixation prediction, context-based learning, interpretability, capsule networks, convolutional neural networks, autonomous driving.

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“…5 Furthermore, it uses the control module to finally obtain control instructions for the steering wheel, accelerator, and brake. 6 Traffic light recognition is an essential part of the perception system. 7 Although the cooperative vehicle infrastructure system (CVIS) based on V2I communication 8 is a reliable and accurate scheme, it still has disadvantages of difficulty in implementation, and the communication easily interferes.…”

Section: Introductionmentioning

confidence: 99%

“…5 Furthermore, it uses the control module to finally obtain control instructions for the steering wheel, accelerator, and brake. 6…”

Section: Introductionmentioning

confidence: 99%

An improved traffic lights recognition algorithm for autonomous driving in complex scenarios

Zeng

Liu³

et al. 2021

International Journal of Distributed Sensor Networks

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Image recognition is susceptible to interference from the external environment. It is challenging to accurately and reliably recognize traffic lights in all-time and all-weather conditions. This article proposed an improved vision-based traffic lights recognition algorithm for autonomous driving, integrating deep learning and multi-sensor data fusion assist (MSDA). We introduce a method to obtain the best size of the region of interest (ROI) dynamically, including four aspects. First, based on multi-sensor data (RTK BDS/GPS, IMU, camera, and LiDAR) acquired in a normal environment, we generated a prior map that contained sufficient traffic lights information. And then, by analyzing the relationship between the error of the sensors and the optimal size of ROI, the adaptively dynamic adjustment (ADA) model was built. Furthermore, according to the multi-sensor data fusion positioning and ADA model, the optimal ROI can be obtained to predict the location of traffic lights. Finally, YOLOv4 is employed to extract and identify the image features. We evaluated our algorithm using a public data set and actual city road test at night. The experimental results demonstrate that the proposed algorithm has a relatively high accuracy rate in complex scenarios and can promote the engineering application of autonomous driving technology.

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Control method simulation and application for autonomous vehicles

Cited by 19 publications

References 3 publications

Exploring Event-driven Dynamic Context for Accident Scene Segmentation

Exploring Event-driven Dynamic Context for Accident Scene Segmentation

Interpretable Global-Local Dynamics for the Prediction of Eye Fixations in Autonomous Driving Scenarios

An improved traffic lights recognition algorithm for autonomous driving in complex scenarios

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