Design and capabilities of the Munich Cognitive Automobile

Goebl, Matthias; Althoff, Matthias; Buss, Martin; Färber, Georg; Hecker, Falk; Heißing, Bernd; Kraus, Sven; Nagel, Robert L.; León, Fernando Puente; Rattei, Florian; Ruß, Martin; Schweitzer, Michael; Thuy, Michael; Wang, Cheng; Wuensche, Hans-Joachim

doi:10.1109/ivs.2008.4621255

Cited by 23 publications

(11 citation statements)

References 23 publications

(21 reference statements)

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“…We take into consideration the difference of reaction time between a human being and a machine with human = 2 s [10] and contrary to human reaction time, there is no work that investigates the average reaction time of automated vehicles, to the best of our knowledge. We will assume machine = 0.3 s from experience with autonomous vehicles, see [6]. The expansion is made by adding circles with varying radius.…”

Section: Evidential Planning Gridmentioning

confidence: 99%

Using evidential occupancy grid for vehicle trajectory planning under uncertainty with tentacles

Mouhagir¹,

Cherfaoui

Talj

et al. 2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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Abstract-The uncertainty in environment perception is one of the challenges that we face in trajectory planning. For autonomous vehicle to be efficient, they need to be able to deal with this kind of uncertainty. In this work, we combine two existing frameworks: the Belief Functions to build evidential occupancy grid and clothoid tentacles for trajectory planning. First, we use evidential grids to represent the environment and the uncertainties which arise from ignorance and errors during the perception process. Secondly, we generate a set of clothoid tentacles in the egocentered reference frame related to the ego-vehicle, those tentacles represent possible local trajectories. Thirdly, we modify the evidential grid in order to take into consideration some traffic rules such as safety distance between vehicles. Then to choose the best tentacle to execute, we use reward system of a Markov Decision Process-like model to evaluate generated tentacles regarding several criteria including uncertainty represented by the evidential grid. Real and simulated data were used to validate the planning algorithm with evidential grids.

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Section: Evidential Planning Gridmentioning

confidence: 99%

Using evidential occupancy grid for vehicle trajectory planning under uncertainty with tentacles

Mouhagir¹,

Cherfaoui

Talj

et al. 2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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show abstract

“…We will assume δ machine = 0.3 s from previous experience of the authors with autonomous vehicles, see e.g. [29], [30]. It is later shown in Tab.…”

Section: B Model Assumptionsmentioning

confidence: 99%

Can automated road vehicles harmonize with traffic flow while guaranteeing a safe distance?

Althoff

Robert

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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Abstract-A frequently raised argument against safelydriving automated vehicles is that they would not harmonize well with traffic flow-unrealistically large headways would invite other traffic participants to cut in and thus put passengers of following automated vehicles at risk. In order to test this hypothesis, we use real data of thousands of vehicles recorded in the United States as part of the Next Generation Simulation (NGSIM) program. To study the hypothesis, we pretend each human-driven vehicle is automated: These automated vehicles drive exactly as the recorded human drivers, but they have a much smaller reaction time and thus can still drive safely in situations that are unsafe for human drivers. The main result is that in only very few cases an automated vehicle would not drive safely, although more than half of the human drivers do not keep a safe distance according to their capabilities. Thus, there is no increased risk of vehicles cutting in-and if they do, automated vehicles are only at risk in around 10% of all cases compared to around 60% for human drivers.

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“…Research on intelligent autonomous robots has mainly focused on machines that perform certain services for humans, such as robot butlers or autonomous cars [7,16,24,26]. The interaction in such systems is restricted to a user interface to give a task to the robot and when acting, such a robot has to ensure the safety of humans in its vicinity.…”

Section: Motivationmentioning

confidence: 99%

Plan-Based Control of Joint Human-Robot Activities

et al. 2010

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Cognition in technical systems is especially relevant for the interaction with humans. We present a newly emerging application for autonomous robots: companion robots that are not merely machines performing tasks for humans, but assistants that achieve joint goals with humans. This collaborative aspect entails specific challenges for AI and robotics. In this article, we describe several planning and action-related problems for human-robot collaboration and point out the challenges to implement cognitive robot assistants. MotivationResearch on intelligent autonomous robots has mainly focused on machines that perform certain services for humans, such as robot butlers or autonomous cars [7,16,24,26]. The interaction in such systems is restricted to a user interface to give a task to the robot and when acting, such a robot has to ensure the safety of humans in its vicinity.A new class of robot applications takes a different view: a robot assistant performs tasks together with a human in order to achieve a joint goal. This approach combines the cognitive capabilities and manual dexterity of humans with the strength, endurance and accuracy of robots. Rosenthal et al. [20] use the term "symbiotic relationship" to describe the reciprocal complementation of skills.On the one hand, the combination of human and robot skills moves useful robot applications closer to the state of marketable products. Whereas a robot butler would be expected to prepare a cup of tea all by itself, a robot assistant might only be expected to bring the kettle and the tea for a person to prepare the tea herself. On the other hand, the close interaction and collaboration with humans requires a range of new cognitive capabilities for robots in order to coordinate the human's and robot's actions in a way that feels natural to people. This paper is concerned with the action side of humanrobot collaboration. This includes AI techniques for planning and plan execution as well as specific action primitives for navigation and joint manipulation tasks. Application ScenariosTo illustrate the idea of assistant robots, we present two application scenarios where a collaboration of a human and a robot can be of substantial help, even if the robot has restricted capabilities.

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Design and capabilities of the Munich Cognitive Automobile

Cited by 23 publications

References 23 publications

Using evidential occupancy grid for vehicle trajectory planning under uncertainty with tentacles

Using evidential occupancy grid for vehicle trajectory planning under uncertainty with tentacles

Can automated road vehicles harmonize with traffic flow while guaranteeing a safe distance?

Plan-Based Control of Joint Human-Robot Activities

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