1
|
Abstract
Since 2017, a research team from the Technical University of Munich has developed a software stack for autonomous driving. The software was used to participate in the Roborace Season Alpha Championship. The championship aims to achieve autonomous race cars competing with different software stacks against each other. In May 2019, during a software test in Modena, Italy, the greatest danger in autonomous driving became reality: A minor change in environmental influences led an extensively tested software to crash into a barrier at speed. Crashes with autonomous vehicles have happened before but a detailed explanation of why software failed and what part of the software was not working correctly is missing in research articles. In this paper we present a general method that can be used to display an autonomous vehicle disengagement to explain in detail what happened. This method is then used to display and explain the crash from Modena. Firstly a brief introduction into the modular software stack that was used in the Modena event, consisting of three individual parts—perception, planning, and control—is given. Furthermore, the circumstancescausing the crash are elaborated in detail. By presented and explaining in detail which softwarepart failed and contributed to the crash we can discuss further software improvements. As a result, we present necessary functions that need to be integrated in an autonomous driving software stack to prevent such a vehicle behavior causing a fatal crash. In addition we suggest an enhancement of the current disengagement reports for autonomous driving regarding a detailed explanation of the software part that was causing the disengagement. In the outlook of this paper we present two additional software functions for assessing the tire and control performance of the vehicle to enhance the autonomous.
Collapse
|
2
|
McGill SG, Rosman G, Ort T, Pierson A, Gilitschenski I, Araki B, Fletcher L, Karaman S, Rus D, Leonard JJ. Probabilistic Risk Metrics for Navigating Occluded Intersections. IEEE Robot Autom Lett 2019. [DOI: 10.1109/lra.2019.2931823] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
3
|
SC-M*: A Multi-Agent Path Planning Algorithm with Soft-Collision Constraint on Allocation of Common Resources. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9194037] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Multi-agent path planning (MAPP) is increasingly being used to address resource allocation problems in highly dynamic, distributed environments that involve autonomous agents. Example domains include surveillance automation, traffic control and others. Most MAPP approaches assume hard collisions, e.g., agents cannot share resources, or co-exist at the same node or edge. This assumption unnecessarily restricts the solution space and does not apply to many real-world scenarios. To mitigate this limitation, this paper introduces a more general class of MAPP problems—MAPP in a soft-collision context. In soft-collision MAPP problems, agents can share resources or co-exist in the same location at the expense of reducing the quality of the solution. Hard constraints can still be modeled by imposing a very high cost for sharing. This paper motivates and defines the soft-collision MAPP problem, and generalizes the widely-used M* MAPP algorithm to support the concept of soft-collisions. Soft-collision M* (SC-M*) extends M* by changing the definition of a collision, so paths with collisions that have a quality penalty below a given threshold are acceptable. For each candidate path, SC-M* keeps track of the reduction in satisfaction level of each agent using a collision score, and it places agents whose collision scores exceed its threshold into a soft-collision set for reducing the score. Our evaluation shows that SC-M* is more flexible and more scalable than M*. It can also handle complex environments that include agents requesting different types of resources. Furthermore, we show the benefits of SC-M* compared with several baseline algorithms in terms of path cost, success rate and run time.
Collapse
|
4
|
Johnson B, Havlak F, Kress‐Gazit H, Campbell M. Experimental Evaluation and Formal Analysis of High‐Level Tasks with Dynamic Obstacle Anticipation on a Full‐Sized Autonomous Vehicle. J FIELD ROBOT 2017. [DOI: 10.1002/rob.21695] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Benjamin Johnson
- Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca, New York 14853
| | - Frank Havlak
- Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca, New York 14853
| | - Hadas Kress‐Gazit
- Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca, New York 14853
| | - Mark Campbell
- Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca, New York 14853
| |
Collapse
|
5
|
Havlak F, Campbell M. Discrete and Continuous, Probabilistic Anticipation for Autonomous Robots in Urban Environments. IEEE T ROBOT 2014. [DOI: 10.1109/tro.2013.2291620] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
6
|
Tang T, Kurkowski J, Lienkamp M. Teleoperated Road Vehicles: A Novel Study on the Effect of Blur on Speed Perception. INT J ADV ROBOT SYST 2013. [DOI: 10.5772/56735] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
The future mobility of urban areas is changing constantly; ideally, vehicles should be able to drive autonomously through traffic. Unfortunately, autonomous vehicles are not yet fully capable of matching human performance. Therefore, the teleoperation of vehicles presents a solution for this task. During teleoperation, a human driver is responsible for driving the vehicle using information transmitted from the vehicle to a working station. Unfortunately, because of the artificial environment in which the operator is located, it is very difficult to achieve high telepresence and accurate speed estimation. It is known that in order to safely drive a vehicle, it is very important to be able to correctly estimate the vehicle's speed. This paper presents a study conducted to quantify the speed perception tendency of a human operator at the working station. Additionally, it is shown that a training process can at least temporarily improve speed perception. Furthermore, the implementation of zoom blur to increase optical flow is shown to positively influence speed perception. Four hypotheses are defined and analysed to study speed perception at an operator's working station. The results are presented and discussed.
Collapse
Affiliation(s)
- Tito Tang
- Lehrstuhl für Fahrzeugtechnik (FTM), Technische Universität München, Munich, Germany
| | - Jan Kurkowski
- Lehrstuhl für Fahrzeugtechnik (FTM), Technische Universität München, Munich, Germany
| | - Markus Lienkamp
- Lehrstuhl für Fahrzeugtechnik (FTM), Technische Universität München, Munich, Germany
| |
Collapse
|
7
|
Hardy J, Campbell M. Contingency Planning Over Probabilistic Obstacle Predictions for Autonomous Road Vehicles. IEEE T ROBOT 2013. [DOI: 10.1109/tro.2013.2254033] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
8
|
Testing Autonomous Robot Control Software Using Procedural Content Generation. LECTURE NOTES IN COMPUTER SCIENCE 2013. [DOI: 10.1007/978-3-642-40793-2_4] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
|
9
|
Werling M, Kammel S, Ziegler J, Gröll L. Optimal trajectories for time-critical street scenarios using discretized terminal manifolds. Int J Rob Res 2011. [DOI: 10.1177/0278364911423042] [Citation(s) in RCA: 140] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper deals with the trajectory generation problem faced by an autonomous vehicle in moving traffic. Being given the predicted motion of the traffic flow, the proposed semi-reactive planning strategy realizes all required long-term maneuver tasks (lane-changing, merging, distance-keeping, velocity-keeping, precise stopping, etc.) while providing short-term collision avoidance. The key to comfortable, human-like as well as physically feasible trajectories is the combined optimization of the lateral and longitudinal movements in street-relative coordinates with carefully chosen cost functionals and terminal state sets (manifolds). The performance of the approach is demonstrated in simulated traffic scenarios.
Collapse
Affiliation(s)
- Moritz Werling
- Department of Applied Computer Science and Automation (AIA), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
- BMW Group Research and Technology, Munich, Germany
| | - Sören Kammel
- Robert Bosch LLC Research and Technology Center, Palo Alto, California, USA
| | - Julius Ziegler
- Department of Measurement and Control (MRT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Lutz Gröll
- Department of Applied Computer Science (IAI), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| |
Collapse
|
10
|
Provably safe navigation for mobile robots with limited field-of-views in dynamic environments. Auton Robots 2011. [DOI: 10.1007/s10514-011-9258-8] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
11
|
Miller I, Campbell M, Huttenlocher D. Efficient Unbiased Tracking of Multiple Dynamic Obstacles Under Large Viewpoint Changes. IEEE T ROBOT 2011. [DOI: 10.1109/tro.2010.2085490] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
12
|
Campbell M, Egerstedt M, How JP, Murray RM. Autonomous driving in urban environments: approaches, lessons and challenges. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2010; 368:4649-4672. [PMID: 20819826 DOI: 10.1098/rsta.2010.0110] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
The development of autonomous vehicles for urban driving has seen rapid progress in the past 30 years. This paper provides a summary of the current state of the art in autonomous driving in urban environments, based primarily on the experiences of the authors in the 2007 DARPA Urban Challenge (DUC). The paper briefly summarizes the approaches that different teams used in the DUC, with the goal of describing some of the challenges that the teams faced in driving in urban environments. The paper also highlights the long-term research challenges that must be overcome in order to enable autonomous driving and points to opportunities for new technologies to be applied in improving vehicle safety, exploiting intelligent road infrastructure and enabling robotic vehicles operating in human environments.
Collapse
|
13
|
Werling M, Gröll L, Bretthauer G. Invariant Trajectory Tracking With a Full-Size Autonomous Road Vehicle. IEEE T ROBOT 2010. [DOI: 10.1109/tro.2010.2052325] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|