Workshop on Online Map Validation and Road Model Creation.

A workshop being held in conjunction with the
IEEE Intelligent Vehicles Symposium - IV2020, June 23rd, 2020, Las Vegas, NV, United States.

View Scope Call for Paper



The goal of this half-day workshop is to form a platform for exchanging ideas, fostering research and connecting both industry and academia in the domain of offline map and online road model creation and validation for autonomous driving.

This workshop will provide the opportunity to discuss about scenarios (highways and urban environments), ways of data collection (i.e. single vehicle and vehicle fleets) as well as data processing (in-vehicle and backend).

To this end we welcome contributions related to the following topics:

Validated Road Models

Creation of validated road models both at runtime and offline, both from single vehicles as well as from a fleet of vehicles.

Map Data Life-Cycle Management

Life-cycle management of map data, i.e. initial creation, change detection, update, verification and deployment.

Mapping Fraud Detection and Prevention

Mapping fraud detection and prevention, i.e. manipulation of traffic signs, fleet data spoofing.

Accepted papers will be published in IEEE Xplore!

Invited Speakers

We are proud to welcome the following invited speakers.

Map validation for autonomous driving – How to describe and verify accuracy, integrity and completeness of HD Maps
Gunnar Gräfe
Managing Director
3D Mapping Solutions
Precise Positioning and Mapping for Driverless Vehicles: Analysis of Sensible 4's All-Weather Solutions for Automated Driving in Urban Environment
Umar Zakir Abdul Hamid
Senior Autonomous Vehicle Engineer
Sensible 4

Call for Papers

We are soliciting high quality papers covering, but not limited to, the following topics .

Paper length should not exceed 6 pages (two additional pages allowed with a fee) according to the IEEE IV 2020 Guidelines.

Each paper will undergo a peer-reviewing process by at least three independent reviewers. Contributions will be reviewed according to relevance, originality and novel ideas, technical soundness and quality of presentation.

Submission Deadline March 14th, 2020 (firm deadline, no extensions)
Submission through PaperPlaza (closed)
Workshop code: hr9k9

Notification Date April 18th, 2020

Camera-ready Deadline May 2nd, 2020


Safety and comfort of autonomous vehicles (AVs) critically depend on the availability of accurate environment models. At runtime, data coming from maps and from sensors are fused together with the goal of providing a unique and consistent view of the environment to software components responsible for maneuver and trajectory planning. One integral part of this view is the road model, which is a geometric and semantic description of the road around the AV, composed of lanes, traffic lights/signs, traffic rules and right of way. From the perspective of a fusion algorithm, maps are sensors with an almost unlimited field of view (FOV) and with a large, potentially unknown, perception delay. The unlimited FOV provided by maps allow AVs to speculatively plan upcoming maneuvers and trajectories on those areas not observed by other sensors yet. In urban scenarios this is extremely important, since traffic can strongly occlude the actual FOV of other sensors. Still, the unknown delay affecting map data may yield to conclusions about the environment model based on outdated information.
For example, geometry of roads can change due to construction work. Thus it is imperative to either verify the validity of map data before fusing them with sensor data, or to build an online road model from in-vehicle sensors alone. Although many approaches for online validation of map exist, e.g, based on Simultaneous localization and mapping (SLAM), very few are suited at validating maps with the stringent accuracy requirements typically found in AV applications. The desired accuracy of maps used by AVs is typically in the range of a few centimeters. Also, algorithms responsible for map validation, need to be able to validate an area of a map, before the AV enters that area. Finally, the large amount of data contained in the high-definition (HD) maps used by AVs, pose a variety of problems in satisfying performance requirements at runtime. Additionally, approaches for online road model creation typically assume highway scenarios and are thus not applicable to urban environments.

Call for Paper


The workshop schedule will be made available after the notification date on April 18th, 2020.


We give our best to make this workshop a success.

Luca Paroloni received the B.Sc. degree in information engineering and the M.Sc. degree in automation engineering from the University of Padova, Padova, Italy, in 2004 and 2006, respectively and the Ph.D. degree in electrical and computer engineering from Carnegie Mellon University, Pittsburgh, PA, in 2012. He is a specialist S/W developer at BMW AG, Munich, Germany, which he joined in 2016. His research interests include functional development for autonomous vehicles, with a special focus on map and localization problems, functional safety, and functional degradation.
Luca Parolini

Sebastian Schneider received his Diploma degree in 2006 from Technical University of Darmstadt, Germany, with a major focus on computer vision and robotics. Since 2014, Sebastian works at BMW AG, Munich, Germany, as a sensor fusion expert in the area of driver assistance systems and autonomous driving. As such he has contributed to the design of the sensor setup as well as the sensor fusion architecture of upcoming level 4 autonomous vehicles. His current research interests include localization, map and road model validation and reinforcement learning.
Sebastian Schneider

Qing Rao received his Bachelor's degree in 2010 from Shanghai Jiao Tong University and his Master's degree in 2012 from TU Munich, with a major focus on computer vision and robotics. In 2019, he received his Ph.D. degree from TU Munich with the dissertation entitled Merging the Virtual and Real in a Car: In-Vehicle Augmented Reality. Since mid-2017, Qing works at BMW AG, Munich, Germany, as a machine learning expert in the area of autonomous driving. His current research interests include road model generation, 3D object detection, and active learning.
Qing Rao

Jenny Yuan holds a master degree from Technical University of Munich in Germany. Her major research direction is related to deep-learning and image processing in the field of computer vision, such as object detection and classification.
Jenny Yuan

Oliver Wasenmüller is full Professor at the Mannheim University for Applied Science. His research is in the intersection of Computer Vision and Artificial Intelligence with a focus on automotive. Previously he was a team leader for "machine vision and autonomous vehicles" at the German Research Center for Artificial Intelligence (DFKI). He is both speaker and reviewer in many scientific conferences in this field and co-organizes the ACM Computer Science in Cars Symposium (CSCS) as well as the IEEE CVPR workshop SAIAD.
Oliver Wasenmüller

Mannheim University for Applied Science