Making Bertha Cooperate–Team AnnieWAY’s Entry to the 2016 Grand Cooperative Driving Challenge

Taş, Ömer Şahin; Salscheider, Niels Ole; Poggenhans, Fabian; Wirges, Sascha; Bandera, Claudio; Zofka, Marc René; Strauß, Tobias; Zöllner, J. Marius; Stiller, Christoph

doi:10.1109/tits.2017.2749974

Cited by 44 publications

(33 citation statements)

References 29 publications

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“…In the case of perception for mobile robots and autonomous (robot) vehicles, such options are not available; thus, its perception systems have to be trained offline. However, besides AI/ML-based algorithms and higher level perception, for autonomous driving applications, environment representation (including multisensor fusion) is of primary concern [50,51].…”

Section: Artificial Intelligence and Machine Learning Applied On Robomentioning

confidence: 99%

Intelligent Robotic Perception Systems

Premebida¹,

Ambruş²,

Márton³

2019

Applications of Mobile Robots

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Robotic perception is related to many applications in robotics where sensory data and artificial intelligence/machine learning (AI/ML) techniques are involved. Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics.

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Section: Artificial Intelligence and Machine Learning Applied On Robomentioning

confidence: 99%

Intelligent Robotic Perception Systems

Premebida¹,

Ambruş²,

Márton³

2019

Applications of Mobile Robots

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“…The winning teams implemented a networkbased publish/subscribe IPC system, which also formed the basis of ROS. Although no information was provided on the software architecture used for the Bertha Benz Memorial Drive [6], Bertha was running ROS during the DARPA Grand Cooperative Challenge in 2016 [13]. Baidu based the first versions of Apollo on an extended ROS by a downwardscompatible message protocol based on Google Protocol Buffers 1 and decentralized node management.…”

Section: B Fortuna Compared To Other Research Vehiclesmentioning

confidence: 99%

“…The presented modules bridge the gap between the hardware and driving stack. We specifically contribute • a detailed discussion on how and for which reasons we modified a production vehicle equipped with stateof-the-art sensors and the access to control lateral and longitudinal motion, starting from a discussion on other VaMP [11] Junior [1] Boss [2] Bertha [6] RACE [10] Halmstad [14] Bertha [13] Apollo [7] fortuna…”

Section: Introductionmentioning

confidence: 99%

Bridging the Gap between Open Source Software and Vehicle Hardware for Autonomous Driving

Keßler

Bernhard

Buechel

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Although many research vehicle platforms for autonomous driving have been built in the past, hardware design, source code and lessons learned have not been made available for the next generation of demonstrators. This raises the efforts for the research community to contribute results based on real-world evaluations as engineering knowledge of building and maintaining a research vehicle is lost. In this paper, we deliver an analysis of our approach to transferring an open source driving stack to a research vehicle.We put the hardware and software setup in context to other demonstrators and explain the criteria that led to our chosen hardware and software design. Specifically, we discuss the mapping of the Apollo driving stack to the system layout of our research vehicle, fortuna, including communication with the actuators by a controller running on a real-time hardware platform and the integration of the sensor setup. With our collection of the lessons learned, we encourage a faster setup of such systems by other research groups in the future.

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“…Occasionally, a mission planner is utilized to extract the itinerary from the road network. This structure is well tried, tested and proven to perform robustly in practice [1], [2], [3]. In this paper we present a motion planning framework following the classical setup of decisionmaking with subsequent trajectory planning which is able to take social factors into account.…”

Section: A Motivationmentioning

confidence: 99%

“…From this information, together with the vehicle dimensions, it is possible to calculate the longitudinal distance ∆s between two vehicles. Furthermore, velocities are required in order to evaluate equations (1) and (2). Therefore, the velocities within the Cartesian frame can be maintained as the orientations of the vehicles might not significantly differ from the heading of the center line.…”

Section: B Reference Trajectory Generationmentioning

confidence: 99%

A Model Based Motion Planning Framework for Automated Vehicles in Structured Environments

Graf

Speidel

Dietmayer

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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A main difficulty in autonomous driving is the assurance of maneuver acceptability by other traffic participants. Thus, knowledge about social interaction needs to be incorporated into the motion planning process. In this paper we present a model based framework to verify the acceptance of considered maneuvers and to plan social compliant motions. Therefore, we fuse two powerful approaches, one for decisionmaking and one for planning and show how the methods benefit from each other. Our method adheres to the classical structure of decision-making with subsequent trajectory planning and is consistent in the sense that both components are subject on the same, identical parametrized driver model. The overall method is real-time capable and the resulting trajectories adhere to kinematic constraints. Thus, the approach is applicable in realworld systems.

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Making Bertha Cooperate–Team AnnieWAY’s Entry to the 2016 Grand Cooperative Driving Challenge

Cited by 44 publications

References 29 publications

Intelligent Robotic Perception Systems

Intelligent Robotic Perception Systems

Bridging the Gap between Open Source Software and Vehicle Hardware for Autonomous Driving

A Model Based Motion Planning Framework for Automated Vehicles in Structured Environments

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