Affordable and Safe High Performance Vehicle Computers with Ultra-Fast On-Board Ethernet for Automated Driving

2018 ◽  
pp. 56-68 ◽  
Author(s):  
Martin Hager ◽  
Przemyslaw Gromala ◽  
Bernhard Wunderle ◽  
Sven Rzepka
Keyword(s):  
High Performance ◽  
Automated Driving

scholarly journals Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project

2021 ◽  
Vol 12 (1) ◽  
pp. 168
Author(s):  
Rihards Novickis ◽  
Aleksandrs Levinskis ◽  
Vitalijs Fescenko ◽  
Roberts Kadikis ◽  
Kaspars Ozols ◽  
...  
Keyword(s):  
High Performance ◽  
Substantial Reduction ◽  
Road Accidents ◽  
Passenger Vehicle ◽  
Real Time Control ◽  
Automated Driving ◽  
Automotive Safety ◽  
Time Control ◽  
Perception System ◽  
Fusion Approach

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project—PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck.

scholarly journals High Definition Map-Based Localization Using ADAS Environment Sensors for Application to Automated Driving Vehicles

2020 ◽  
Vol 10 (14) ◽  
pp. 4924
Author(s):  
Donghoon Shin ◽  
Kang-moon Park ◽  
Manbok Park
Keyword(s):  
High Performance ◽  
Low Cost ◽  
Autonomous Driving ◽  
Feature Representation ◽  
Map Matching ◽  
Localization Algorithm ◽  
Automated Driving ◽  
High Definition ◽  
Vehicle Position ◽  
Near Future

This paper presents high definition (HD) map-based localization using advanced driver assistance system (ADAS) environment sensors for application to automated driving vehicles. A variety of autonomous driving technologies are being developed using expensive and high-performance sensors, but limitations exist due to several practical issues. In respect of the application of autonomous driving cars in the near future, it is necessary to ensure autonomous driving performance by effectively utilizing sensors that are already installed for ADAS purposes. Additionally, the most common localization algorithm, which is usually used lane information only, has a highly unstable disadvantage in the absence of that information. Therefore, it is essential to ensure localization performance with other road features such as guardrails when there are no lane markings. In this study, we would like to propose a localization algorithm that could be implemented in the near future by using low-cost sensors and HD maps. The proposed localization algorithm consists of several sections: environment feature representation with low-cost sensors, digital map analysis and application, position correction based on map-matching, designated validation gates, and extended Kalman filter (EKF)-based localization filtering and fusion. Lane information is detected by monocular vision in front of the vehicle. A guardrail is perceived by radar by distinguishing low-speed object measurements and by accumulating several steps to extract wall features. These lane and guardrail information are able to correct the host vehicle position by using the iterative closest point (ICP) algorithm. The rigid transformation between the digital high definition map (HD map) and environment features is calculated through ICP matching. Each corrected vehicle position by map-matching is selected and merged based on EKF with double updating. The proposed algorithm was verified through simulation based on actual driving log data.

Safety for Automated Driving with High Performance ECUs

ELIV 2019 ◽  
2019 ◽  
pp. 395-406
Author(s):  
M. Oertel ◽  
J. Wolf
Keyword(s):  
High Performance ◽  
Automated Driving

scholarly journals High Performance Pavement Markings Enhancing Camera And LiDAR Detection

2021 ◽  
Vol 1202 (1) ◽  
pp. 012033
Author(s):  
Gernot Sauter ◽  
Marcel Doring ◽  
Rik Nuyttens
Keyword(s):  
High Performance ◽  
Test Methods ◽  
Infrared Light ◽  
Automated Driving ◽  
Joint Research ◽  
Pavement Markings ◽  
The Road ◽  
Joint Research Project ◽  
Wet Condition ◽  
Video Sensors

Abstract It is well known that camera and video sensors have limitations in detecting pavement markings under certain conditions e.g. glare from sunlight or other vehicles, rain, fog etc. First generations of lane keeping systems depend on visual light. Erroneous detection is also resulting from irregular road surfaces such as glossy bitumen sealing strips, rain puddles or simply worn asphalt. The role of higher performing markings and better visual camera detection has been studied with Vedecom France. LiDAR (light detection and ranging) technology could help to fill remaining gaps, as it actively sends out IR (infrared) light, that returns reliable images of the road scenario and pavement markings both day and nighttime. In order to evaluate the opportunities of LiDAR technology for the detection of road markings, 3M Company and the University of Applied Sciences in Dresden decided to work together in a joint research project. All-Weather Elements AWE, are the latest development of high-performance optics, using high index beads to provide reflectivity both in dry and wet condition. It could be determined that high performance markings help to increase the level of detection by both camera and LiDAR sensors. The AWE marking was detected from significantly longer distances, especially in wet and rainy conditions. In combination with common camera based LKA and LDW systems, the LiDAR sensors can increase the overall detection rate of pavement markings. This is especially important for vehicles with higher SAE levels of automated driving and can support the overall safety of vehicles. The research also evaluated existing test methods for wet and rain reflectivity in EN 1436 and ASTM E 2832 and how measured performance correlates with LiDAR detection.

h²ECU: A High-Performance and Heterogeneous Electronic Control Unit for Automated Driving

IEEE Micro ◽  
2018 ◽  
Vol 38 (5) ◽  
pp. 53-62
Author(s):  
Xiebing Wang ◽  
Kai Huang ◽  
Long Chen ◽  
Alois Knoll
Keyword(s):  
High Performance ◽  
Electronic Control Unit ◽  
Control Unit ◽  
Electronic Control ◽  
Automated Driving

Neural network vehicle models for high-performance automated driving

2019 ◽  
Vol 4 (28) ◽  
pp. eaaw1975 ◽  
Author(s):  
Nathan A. Spielberg ◽  
Matthew Brown ◽  
Nitin R. Kapania ◽  
John C. Kegelman ◽  
J. Christian Gerdes
Keyword(s):  
Neural Network ◽  
Feedback Control ◽  
Physical Model ◽  
Network Structure ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Human Beings ◽  
Automated Vehicles ◽  
Automated Driving ◽  
The Road

Automated vehicles navigate through their environment by first planning and subsequently following a safe trajectory. To prove safer than human beings, they must ultimately perform these tasks as well or better than human drivers across a broad range of conditions and in critical situations. We show that a feedforward-feedback control structure incorporating a simple physics-based model can be used to track a path up to the friction limits of the vehicle with performance comparable with a champion amateur race car driver. The key is having the appropriate model. Although physics-based models are useful in their transparency and intuition, they require explicit characterization around a single operating point and fail to make use of the wealth of vehicle data generated by autonomous vehicles. To circumvent these limitations, we propose a neural network structure using a sequence of past states and inputs motivated by the physical model. The neural network achieved better performance than the physical model when implemented in the same feedforward-feedback control architecture on an experimental vehicle. More notably, when trained on a combination of data from dry roads and snow, the model was able to make appropriate predictions for the road surface on which the vehicle was traveling without the need for explicit road friction estimation. These findings suggest that the network structure merits further investigation as the basis for model-based control of automated vehicles over their full operating range.

A High Performance Energy Analyzer for Use in Electron Scanning Microscopy

1969 ◽  
Vol 27 ◽  
pp. 14-15
Author(s):  
A. V. Crewe ◽  
M. Isaacson ◽  
D. Johnson
Keyword(s):  
High Performance ◽  
Vertical Plane ◽  
Median Plane ◽  
Electron Gun ◽  
Uniform Field ◽  
Loss Mechanism ◽  
Electron Scanning Microscopy ◽  
Sector Type ◽  
Double Focusing ◽  
Electron Energy Loss

A double focusing magnetic spectrometer has been constructed for use with a field emission electron gun scanning microscope in order to study the electron energy loss mechanism in thin specimens. It is of the uniform field sector type with curved pole pieces. The shape of the pole pieces is determined by requiring that all particles be focused to a point at the image slit (point 1). The resultant shape gives perfect focusing in the median plane (Fig. 1) and first order focusing in the vertical plane (Fig. 2).

Vacuum System to Minimize the Specimen Contamination of High-Performance EM

1977 ◽  
Vol 35 ◽  
pp. 68-69
Author(s):  
N. Yoshimura ◽  
K. Shirota ◽  
T. Etoh
Keyword(s):  
Electron Microscope ◽  
High Speed ◽  
High Performance ◽  
High Vacuum ◽  
Vacuum System ◽  
Pump System ◽  
Pumping System ◽  
Diffusion Pump ◽  
Almost All ◽  
Cascade Type

One of the most important requirements for a high-performance EM, especially an analytical EM using a fine beam probe, is to prevent specimen contamination by providing a clean high vacuum in the vicinity of the specimen. However, in almost all commercial EMs, the pressure in the vicinity of the specimen under observation is usually more than ten times higher than the pressure measured at the punping line. The EM column inevitably requires the use of greased Viton O-rings for fine movement, and specimens and films need to be exchanged frequently and several attachments may also be exchanged. For these reasons, a high speed pumping system, as well as a clean vacuum system, is now required. A newly developed electron microscope, the JEM-100CX features clean high vacuum in the vicinity of the specimen, realized by the use of a CASCADE type diffusion pump system which has been essentially improved over its predeces- sorD employed on the JEM-100C.

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