Environment perception for inner-city driver assistance and highly-automated driving

2014 ◽  
Author(s):  
Claudius Glaser ◽  
Thomas Paul Michalke ◽  
Lutz Burkle ◽  
Frank Niewels
Keyword(s):  
Inner City ◽  
Driver Assistance ◽  
Automated Driving ◽  
Environment Perception ◽  
Highly Automated Driving

scholarly journals Object-Level Fusion for Surround Environment Perception in Automated Driving Applications

2017 ◽  
Author(s):  
Michael Aeberhard
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Driver Assistance ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Sensor Data Fusion ◽  
Level Sensor ◽  
Environment Perception ◽  
High Level ◽  
Level Fusion

Driver assistance systems have increasingly relied on more sensors for new functions. As advanced driver assistance system continue to improve towards automated driving, new methods are required for processing the data in an efficient and economical manner from the sensors for such complex systems. In this thesis, an environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion. A scalable high-level fusion architecture is developed for fusing object data from several sensors in a single system. The developed high-level sensor data fusion architecture and its algorithms are evaluated using a prototype vehicle equipped with 12 sensors for surround environment perception. The work presented in this thesis has been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic. Contents Abbreviations VIII List of Symbols X Abs...

scholarly journals Dynamic scan paths investigations under manual and highly automated driving

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jordan Navarro ◽  
Otto Lappi ◽  
François Osiurak ◽  
Emma Hernout ◽  
Catherine Gabaude ◽  
...  
Keyword(s):  
Relative Frequency ◽  
Human Locomotion ◽  
Visual Scanning ◽  
Automated Driving ◽  
The Gaze ◽  
Visual Inputs ◽  
Gaze Patterns ◽  
Gaze Behaviour ◽  
Scan Paths ◽  
Highly Automated Driving

AbstractActive visual scanning of the scene is a key task-element in all forms of human locomotion. In the field of driving, steering (lateral control) and speed adjustments (longitudinal control) models are largely based on drivers’ visual inputs. Despite knowledge gained on gaze behaviour behind the wheel, our understanding of the sequential aspects of the gaze strategies that actively sample that input remains restricted. Here, we apply scan path analysis to investigate sequences of visual scanning in manual and highly automated simulated driving. Five stereotypical visual sequences were identified under manual driving: forward polling (i.e. far road explorations), guidance, backwards polling (i.e. near road explorations), scenery and speed monitoring scan paths. Previously undocumented backwards polling scan paths were the most frequent. Under highly automated driving backwards polling scan paths relative frequency decreased, guidance scan paths relative frequency increased, and automation supervision specific scan paths appeared. The results shed new light on the gaze patterns engaged while driving. Methodological and empirical questions for future studies are discussed.

scholarly journals Highly Automated Driving, Secondary Task Performance, and Driver State

2012 ◽  
Vol 54 (5) ◽  
pp. 762-771 ◽  
Author(s):  
Natasha Merat ◽  
A. Hamish Jamson ◽  
Frank C. H. Lai ◽  
Oliver Carsten
Keyword(s):  
Task Performance ◽  
Secondary Task ◽  
Automated Driving ◽  
Highly Automated Driving
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