Applications of Road Edge Information for Advanced Driver Assistance Systems and Autonomous Driving

2017 ◽  
pp. 71-86 ◽  
Author(s):  
Toshiharu Sugawara ◽  
Heiko Altmannshofer ◽  
Shinji Kakegawa
Keyword(s):  
Autonomous Driving ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Edge Information ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

scholarly journals Anticipatory experience in everyday autonomous driving

2020 ◽  
Vol 24 (6) ◽  
pp. 747-762
Author(s):  
Thomas Lindgren ◽  
Vaike Fors ◽  
Sarah Pink ◽  
Katalin Osz
Keyword(s):  
User Experience ◽  
Autonomous Driving ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Everyday Lives ◽  
Ethnographic Studies ◽  
Assistance Systems ◽  
Everyday Context

AbstractIn this paper, we discuss how people’s user experience (UX) of autonomous driving (AD) cars can be understood as a shifting anticipatory experience, as people experience degrees of AD through evolving advanced driver assistance systems (ADAS) in their everyday context. We draw on our ethnographic studies of five families, who had access to AD research cars with evolving ADAS features in their everyday lives for a duration of 1½ years. Our analysis shows that people gradually adopt AD cars, through a process that involves anticipating if they can trust them, what the ADAS features will do and what the longer-term technological possibilities will be. It also showed that this anticipatory UX occurs within specific socio-technical and environmental circumstances, which could not be captured easily in experimental settings. The implication is that studying anticipation offers us new insights into how people adopt AD in their everyday commute driving.

Miniaturized Advanced Driver Assistance Systems: A Low-Cost Educational Platform for Advanced Driver Assistance Systems and Autonomous Driving

2021 ◽  
Vol 38 (3) ◽  
pp. 105-114
Author(s):  
Michael Gerstmair ◽  
Martin Gschwandtner ◽  
Rainer Findenig ◽  
Oliver Lang ◽  
Alexander Melzer ◽  
...  
Keyword(s):  
Low Cost ◽  
Autonomous Driving ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

Sensor Fusion for Advanced Driver Assistance Systems

2017 ◽  
Author(s):  
Mario Amoruso ◽  
Stefano Caiola ◽  
Giuseppe Doronzo ◽  
Marino Difino
Keyword(s):  
Sensor Fusion ◽  
Autonomous Driving ◽  
White Paper ◽  
System Level ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Fusion Concept ◽  
Assistance Systems ◽  
Initial Research

As vehicles move toward autonomous capability, there is a rising need for hardware-in-the loop (HIL) testing to validate and verify the functionality of advanced driver assistance systems (ADAS), which are anticipated to play a central role in autonomous driving. This white paper gives an overview of the ADAS HIL with sensor fusion concept, shares main takeaways from initial research efforts, and highlights key system-level elements used to implement the application.

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

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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