Safety Verification of Automated Driving Systems

Kianfar, Roozbeh; Falcone, Paolo; Fredriksson, Jonas

doi:10.1109/mits.2013.2278405

Cited by 41 publications

(17 citation statements)

References 9 publications

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“…The lane boundary information for lanelets can be directly obtained from an opensource map (e.g. OpenSteetMap 2 ) by e.g. importing a raw map into the free editor JavaOpenStreetMap (JOSM) 3 .…”

Section: A Road Network Representationmentioning

confidence: 99%

“…However, the set-based computations in [45] are heuristic and thus not applicable to a formal analysis. Reachability analysis is also applied to driving assistant systems [2], [46] rather than using it for occupancy prediction as done in this work. Reachable sets have also been applied to mobile robots, typically assuming simple models that are overly simplified for road traffic applications: [47] assumes intervals on possible velocities in all directions; more advanced models assume intervals on the velocity and acceleration [48].…”

Section: Introductionmentioning

confidence: 99%

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Set-Based Prediction of Traffic Participants on Arbitrary Road Networks

Althoff

Magdici

2016

IEEE Trans. Intell. Veh.

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Abstract-Safety is of paramount importance in automated driving. One of the main challenges ensuring safety is the unknown future behavior of surrounding traffic participants. Previous works ignore this uncertainty or often address it by computing probability distributions of other traffic participants over time. Probabilistic approaches make it possible to predict the collision probability with other traffic participants, but cannot formally guarantee (i.e. cannot mathematically prove for given assumptions) whether a planned maneuver is collision-free. Our approach addresses exactly this problem: instead of computing probability distributions, we compute an over-approximation of all possible occupancies of surrounding traffic participants over time. This makes it possible to prove whether an automated vehicle can possibly collide with other traffic participants. The presented algorithm for occupancy prediction works on arbitrary road networks and produces results within a fraction of the prediction horizon. Experiments based on real-world data validate our approach and show that we could not find a behavior of a traffic participant that is not enclosed in our prediction.

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Section: A Road Network Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Set-Based Prediction of Traffic Participants on Arbitrary Road Networks

Althoff

Magdici

2016

IEEE Trans. Intell. Veh.

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“…In this section, we derive a set of sufficient LMI conditions for the ensuring the performance objective in (13) for the closed-loop system described by (11). The result of this section provides an alternative to the relevant result in our previous work [13].…”

Section: Synthesis Of Leader and Predecessor Feedback For String mentioning

confidence: 99%

“…Although α is a free scalar, a natural choice will arise in the course of our derivations. In order to derive dilated LMI conditions that ensure (13), we first obtain an alternative expression of the closed-loop in (11). To this end, we introduce…”

Section: Synthesis Of Leader and Predecessor Feedback For String mentioning

confidence: 99%

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Joint synthesis of dynamic feed-forward and static state feedback for platoon control

Köroğlu

Falcone

2014

53rd IEEE Conference on Decision and Control

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Joint synthesis of static state-feedback and dynamic feed-forward is considered for a homogenous platoon operating under the leader and predecessor following scheme. The problem is formulated with two H ∞ -type performance objectives. The first objective requires the first vehicle to respond in a desirable way to the maneuvers of the leader and it is to be realized by a joint synthesis of the feed-forward filter together with a state feedback gain. Novel LMI conditions are derived for this objective by fixing the order and the poles of the filter. The second objective aims at preventing the amplification of the errors backward along the platoon, while also maintaining robustness against communication problems with the leader. New sufficient LMI conditions are provided for this objective, based on which leader and predecessor feedback gain matrices can be constructed. 53rd IEEE Conference on Decision and Control

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Towards Predictable Vehicular Networks

Schiller

2016

Intelligent Transportation Systems

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Safety Verification of Automated Driving Systems

Cited by 41 publications

References 9 publications

Set-Based Prediction of Traffic Participants on Arbitrary Road Networks

Set-Based Prediction of Traffic Participants on Arbitrary Road Networks

Joint synthesis of dynamic feed-forward and static state feedback for platoon control

Towards Predictable Vehicular Networks

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