Autonomous driving at Ulm University: A modular, robust, and sensor-independent fusion approach

Kunz, Felix; Nuß, Dominik; Wiest, Jürgen; Deusch, Hendrik; Reuter, Stephan; Gritschneder, Franz; Scheel, Alexander; Stübler, Manuel; Bach, Martin; Hatzelmann, Patrick; Wild, C.; Dietmayer, Klaus

doi:10.1109/ivs.2015.7225761

Cited by 108 publications

(64 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The localization inside this map was then performed in realtime with a 2D histogram filter for a 10-cm accuracy on many kilometers. The approach of Ulm University is exposed in [267]. A map, composed of MSER features coming from vision and of a laser grid, is built beforehand and georeferenced using a RTK-GPS.…”

Section: Vehiclesmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

700

366

View full text Add to dashboard Cite

Abstract-In this article, we propose a survey of the Simultaneous Localization And Mapping field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multi-vehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various largescale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

show abstract

Section: Vehiclesmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

700

366

View full text Add to dashboard Cite

show abstract

“…The data was recorded using the autonomous vehicle of Ulm University [16]. The car is equipped with a differential global positioning system (DGPS) [17] used for high precision ego motion estimation and localization.…”

Section: Methodsmentioning

confidence: 99%

“…However, if no leading vehicle α + 1 is included in the environment model, (8), (9) and (10) are used to update the input vector of vehicle α while the acceleration of the unknown object is modeled with (16). Also for the input vector, sampling invalid or implausible values must be encountered, e.g.…”

Section: ) Prediction Initializationmentioning

confidence: 99%

Probabilistic long-term prediction for autonomous vehicles

Hoermann

Stumper

Dietmayer

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

View full text Add to dashboard Cite

Abstract-Long-term prediction of traffic participants is crucial to enable autonomous driving on public roads. The quality of the prediction directly affects the frequency of trajectory planning. With a poor estimation of the future development, more computational effort has to be put in re-planning, and a safe vehicle state at the end of the planning horizon is not guaranteed. A holistic probabilistic prediction, considering inputs, results and parameters as random variables, highly reduces the problem. A time frame of several seconds requires a probabilistic description of the scene evolution, where uncertainty or accuracy is represented by the trajectory distribution. Following this strategy, a novel evaluation method is needed, coping with the fact, that the future evolution of a scene is also uncertain. We present a method to evaluate the probabilistic prediction of real traffic scenes with varying start conditions. The proposed prediction is based on a particle filter, estimating behavior describing parameters of a microscopic traffic model. Experiments on real traffic data with random leading vehicles show the applicability in terms of convergence, enabling long-term prediction using forward propagation.

show abstract

“…In unstructered environment a common method is to first search a drivable path and afterwards an according velocity profile is generated [1]. An approved concept for structured environment is to sample quintic polynomials in a Frénet frame which allows longitudinal and lateral movement along a given path [2], [4]. To do so, target states are determined by a rule-based heuristic depending on the current behavior state.…”

Section: Related Workmentioning

confidence: 99%

Towards Courteous Behavior and Trajectory Planning for Automated Driving

Speidel

Graf

Phan-Huu³

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Self Cite

View full text Add to dashboard Cite

Efficient behavior and trajectory planning is one of the major challenges for automated driving. Especially intersection scenarios are very demanding due to their complexity arising from the variety of maneuver possibilities and other traffic participants. A key challenge is to generate behaviors which optimize the comfort and progress of the ego vehicle but at the same time are not too aggressive towards other traffic participants. In order to maintain real time capability for courteous behavior and trajectory planning, an efficient formulation of the optimal control problem and corresponding solving algorithms are required. Consequently, a novel planning framework is presented which considers comfort and progress as well as the courtesy of actions in a graph-based behavior planning module. Utilizing the low level trajectory generation, the behavior result can be further optimized for driving comfort while satisfying constraints over the whole planning horizon. According experiments show the practicability and real time capability of the framework.

show abstract

Autonomous driving at Ulm University: A modular, robust, and sensor-independent fusion approach

Cited by 108 publications

References 33 publications

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Probabilistic long-term prediction for autonomous vehicles

Towards Courteous Behavior and Trajectory Planning for Automated Driving

Contact Info

Product

Resources

About