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20172023
most citedAssessment of Deep Convolutional Neural Networks for Road Surface Classification

121 citations · 419 across the 24 of their papers we have counts for

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Showing 2018Show all

6 papers · 1 filter

eess.SY2018★ 4 cited

Investigating Functional Redundancies in the Context of Vehicle Automation - A Trajectory Tracking Perspective

Torben Stolte, Tianyu Liao, Matthias Nee +2

Level 3+ automated driving implies highest safety demands for the entire vehicle automation functionality. For the part of trajectory tracking, functional redundancies among all av…

eess.SY2018

Identification of Potential Hazardous Events for an Unmanned Protective Vehicle

Gerrit Bagschik, Andreas Reschka, Torben Stolte +1

The project Automated Unmanned Protective Vehicle for Highway Hard Shoulder Road Works (aFAS) aims to develop an unmanned protective vehicle to reduce the risk of injuries due to c…

eess.SY2018

A System's Perspective Towards an Architecture Framework for Safe Automated Vehicles

Gerrit Bagschik, Marcus Nolte, Susanne Ernst +1

With an increasing degree of automation, automated vehicle systems become more complex in terms of functional components as well as interconnected hardware and software components.…

cs.CV2018★ 121 cited

Assessment of Deep Convolutional Neural Networks for Road Surface Classification

Marcus Nolte, Nikita Kister, Markus Maurer

When parameterizing vehicle control algorithms for stability or trajectory control, the road-tire friction coefficient is an essential model parameter when it comes to control perf…

eess.SY2018★ 16 cited

Representing the Unknown - Impact of Uncertainty on the Interaction between Decision Making and Trajectory Generation

Marcus Nolte, Susanne Ernst, Jan Richelmann +1

Even though motion planning for automated vehicles has been extensively discussed for more than two decades, it is still a highly active field of research with a variety of differe…

cs.SE2018

Scenarios for Development, Test and Validation of Automated Vehicles

Till Menzel, Gerrit Bagschik, Markus Maurer

The ISO 26262 standard from 2016 represents the state of the art for a safety-guided development of safety-critical electric/electronic vehicle systems. These vehicle systems inclu…