Development of ADAS perception applications in ROS and "Software-In-the-Loop" validation with CARLA simulator

Higher levels of autonomous driving are bringing sophisticated requirements and unpredicted challenges. In order to solve these problems, the set of functionalities in modern vehicles is growing in terms of algorithmic complexity and required hardware. The risk of testing implemented solutions in real world is high, expensive and time consuming. This is the reason for virtual automotive simulation tools for testing are heavily acclaimed. Original Equipment Manufacturers (OEMs) use these tools to create closed sense, compute, act loop to have realistic testing scenarios. Production software is tested against simulated sensing data. Based on these inputs a set of actions is produced and simulated which generates consequences that are evaluated. This creates a possibility for OEMs to minimize design errors and optimize costs of the vehicle production before any physical prototypes are produced. This paper presents the development of simple C++/Python perception applications that can be used in driver assistance functionalities. Using ROS as a prototyping platform these applications are validated and tested with "Software-In-the Loop" (SIL) method. CARLA simulator is used as a generator for input data and output commands of the autonomous platform are executed as simulated actions within simulator. Validation is done by connecting Autoware autonomous platform with CARLA simulator in order to test against various scenes in which applications are applicable. Vision based lane detection, which is one of the prototypes, is also tested in a real world scenario to demonstrate the applicability of algorithms developed with simulators to real-time processing

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Moving Object Detection Based on Optical Flow Estimation and a Gaussian Mixture Model for Advanced Driver Assistance Systems

Sensors ◽

10.3390/s19143217 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3217 ◽

Cited By ~ 9

Author(s):

Jaechan Cho ◽

Yongchul Jung ◽

Dong-Sun Kim ◽

Seongjoo Lee ◽

Yunho Jung

Keyword(s):

Object Detection ◽

Real Time ◽

Moving Object Detection ◽

Moving Object ◽

Driver Assistance ◽

Driver Assistance Systems ◽

Moving Camera ◽

Real Time Processing ◽

Time Processing ◽

Assistance Systems

Most approaches for moving object detection (MOD) based on computer vision are limited to stationary camera environments. In advanced driver assistance systems (ADAS), however, ego-motion is added to image frames owing to the use of a moving camera. This results in mixed motion in the image frames and makes it difficult to classify target objects and background. In this paper, we propose an efficient MOD algorithm that can cope with moving camera environments. In addition, we present a hardware design and implementation results for the real-time processing of the proposed algorithm. The proposed moving object detector was designed using hardware description language (HDL) and its real-time performance was evaluated using an FPGA based test system. Experimental results demonstrate that our design achieves better detection performance than existing MOD systems. The proposed moving object detector was implemented with 13.2K logic slices, 104 DSP48s, and 163 BRAM and can support real-time processing of 30 fps at an operating frequency of 200 MHz.

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Driver assistance systems - real time processing solutions

IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683) ◽

10.1109/ivs.2003.1212971 ◽

2004 ◽

Author(s):

K. Parnell

Keyword(s):

Real Time ◽

Driver Assistance ◽

Driver Assistance Systems ◽

Real Time Processing ◽

Time Processing ◽

Assistance Systems

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RS-Lane: A Robust Lane Detection Method Based on ResNeSt and Self-Attention Distillation for Challenging Traffic Situations

Journal of Advanced Transportation ◽

10.1155/2021/7544355 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Ronghui Zhang ◽

Yueying Wu ◽

Wanting Gou ◽

Junzhou Chen

Keyword(s):

Detection Method ◽

Autonomous Driving ◽

Lane Detection ◽

Feature Representation ◽

Driver Assistance ◽

Driver Assistance Systems ◽

Advanced Driver Assistance Systems ◽

Assistance Systems ◽

Light And Shadow ◽

Instance Segmentation

Lane detection plays an essential part in advanced driver-assistance systems and autonomous driving systems. However, lane detection is affected by many factors such as some challenging traffic situations. Multilane detection is also very important. To solve these problems, we proposed a lane detection method based on instance segmentation, named RS-Lane. This method is based on LaneNet and uses Split Attention proposed by ResNeSt to improve the feature representation on slender and sparse annotations like lane markings. We also use Self-Attention Distillation to enhance the feature representation capabilities of the network without adding inference time. RS-Lane can detect lanes without number limits. The tests on TuSimple and CULane datasets show that RS-Lane has achieved comparable results with SOTA and has improved in challenging traffic situations such as no line, dazzle light, and shadow. This research provides a reference for the application of lane detection in autonomous driving and advanced driver-assistance systems.

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A real-time processing system for determination of metabolic rates of a suited astronaut

10.2514/6.1969-993 ◽

1969 ◽

Author(s):

D. BILLINGSLEY ◽

G. HUMBERT ◽

R. KELLY

Keyword(s):

Real Time ◽

Processing System ◽

Metabolic Rates ◽

Real Time Processing ◽

Time Processing

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Integration of 3D Modeling and Real-time Processing for Enhancement of Anisotropic Formation Evaluation with Borehole Multicomponent Induction Measurements

10.2523/17124-ms ◽

2013 ◽

Author(s):

Junsheng Hou ◽

Luis San Martin ◽

David Torres

Keyword(s):

Real Time ◽

3D Modeling ◽

Real Time Processing ◽

Time Processing ◽

Formation Evaluation ◽

Anisotropic Formation

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GPU Acceleration of Algorithm to Design Directional Volumetric Display for Real-time Processing

Proceedings of the International Display Workshops ◽

10.36463/idw.2019.0989 ◽

2019 ◽

pp. 989

Author(s):

Daiki Matsumoto ◽

Ryuji Hirayama ◽

Naoto Hoshikawa ◽

Hirotaka Nakayama ◽

Tomoyoshi Shimobaba ◽

...

Keyword(s):

Real Time ◽

Gpu Acceleration ◽

Real Time Processing ◽

Time Processing ◽

Volumetric Display

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Putting up barriers

10.1093/oso/9780198785156.003.0001 ◽

2017 ◽

Author(s):

David J. Lobina

Keyword(s):

Knowledge Base ◽

Real Time ◽

Language Processing ◽

Cognitive Domain ◽

Linguistic Knowledge ◽

Levels Of Explanation ◽

Real Time Processing ◽

Time Processing ◽

Neural Underpinnings ◽

Different Levels

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.

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Limits on expectation-based processing: Use of grammatical aspect for co-reference in L2

Applied Psycholinguistics ◽

10.1017/s0142716420000582 ◽

2020 ◽

pp. 1-25

Author(s):

Theres Grüter ◽

Hannah Rohde

Keyword(s):

Real Time ◽

Language Processing ◽

Judgment Task ◽

Linguistic Knowledge ◽

Real Time Processing ◽

Time Processing ◽

Grammatical Aspect ◽

Level Information ◽

Native Speakers Of English ◽

Reference Processing

Abstract This study examines the use of discourse-level information to create expectations about reference in real-time processing, testing whether patterns previously observed among native speakers of English generalize to nonnative speakers. Findings from a visual-world eye-tracking experiment show that native (L1; N = 53) but not nonnative (L2; N = 52) listeners’ proactive coreference expectations are modulated by grammatical aspect in transfer-of-possession events. Results from an offline judgment task show these L2 participants did not differ from L1 speakers in their interpretation of aspect marking on transfer-of-possession predicates in English, indicating it is not lack of linguistic knowledge but utilization of this knowledge in real-time processing that distinguishes the groups. English proficiency, although varying substantially within the L2 group, did not modulate L2 listeners’ use of grammatical aspect for reference processing. These findings contribute to the broader endeavor of delineating the role of prediction in human language processing in general, and in the processing of discourse-level information among L2 users in particular.

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