SPOT: A tool for set-based prediction of traffic participants

Koschi, Markus; Althoff, Matthias

doi:10.1109/ivs.2017.7995951

Cited by 69 publications

(63 citation statements)

References 21 publications

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“…The occupancy sets of all vehicles are predicted using our tool SPOT 1 [33]. This tool is based on reachability analysis and allows one to efficiently over-approximate the set of future occupancies of traffic participants under given assumptions.…”

Section: A Verification Of the Overtaking Trajectorymentioning

confidence: 99%

Enhancing motion safety by identifying safety-critical passageways

Pek

Koschi

Werling

et al. 2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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Abstract-Safety is the most important aspect of systems which have to perform collision-free motions in dynamic environments. Formal verification methods, such as reachability analysis, are capable of guaranteeing safety for a given model and given assumptions (e. g. bounded velocity and acceleration). However, certain assumptions can be violated by dynamic obstacles during the execution of the verified motion plan, exposing the system to potential collisions. To compensate for the invalidated verification, this paper introduces the Point of No Return (PNR) and the Point of Guaranteed Arrival (PGA) by incorporating invariably safe sets. These concepts allow one to divide the planned trajectory into safe sections and safety-critical passageways. For the former, we are able to provide safety guarantees for an infinite time horizon. For the latter, we present a method to minimize such safety-critical passageways prior to execution and thus reduce the risk of potential collisions if assumptions are violated during execution. The safety benefits are highlighted by a numerical example of overtaking maneuvers of self-driving vehicles.

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Section: A Verification Of the Overtaking Trajectorymentioning

confidence: 99%

Enhancing motion safety by identifying safety-critical passageways

Pek

Koschi

Werling

et al. 2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

Self Cite

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“…In the following we will enhance the reachable set approximations introduced by [2] and released as source code [15] for such initial state sets.…”

mentioning

confidence: 99%

Tackling Occlusions & Limited Sensor Range with Set-based Safety Verification

Orzechowski

Meyer

Lauer

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Provable safety is one of the most critical challenges in automated driving. The behavior of numerous traffic participants in a scene cannot be predicted reliably due to complex interdependencies and the indiscriminate behavior of humans. Additionally, we face high uncertainties and only incomplete environment knowledge. Recent approaches minimize risk with probabilistic and machine learning methods -even under occlusions. These generate comfortable behavior with good traffic flow, but cannot guarantee safety of their maneuvers. Therefore, we contribute a safety verification method for trajectories under occlusions. The field-of-view of the ego vehicle and a map are used to identify critical sensing field edges, each representing a potentially hidden obstacle. The state of occluded obstacles is unknown, but can be over-approximated by intervals over all possible states. Then set-based methods are extended to provide occupancy predictions for obstacles with state intervals. The proposed method can verify the safety of given trajectories (e.g. if they ensure collision-free fail-safe maneuver options) w.r.t. arbitrary safe-state formulations. The potential for provably safe trajectory planning is shown in three evaluative scenarios.

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“…For occupancy sets we only allow polygons as a representation that can be obtained from our tool SPOT [51]. Please note that one can also represent known behavior by evolving occupancy sets, which do not change their size over time.…”

Section: B Obstaclesmentioning

confidence: 99%

CommonRoad: Composable benchmarks for motion planning on roads

Althoff

Koschi

Manzinger

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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247

166

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Abstract-Numerical experiments for motion planning of road vehicles require numerous components: vehicle dynamics, a road network, static obstacles, dynamic obstacles and their movement over time, goal regions, a cost function, etc. Providing a description of the numerical experiment precise enough to reproduce it might require several pages of information. Thus, only key aspects are typically described in scientific publications, making it impossible to reproduce results-yet, reproducibility is an important asset of good science. Composable benchmarks for motion planning on roads (CommonRoad) are proposed so that numerical experiments are fully defined by a unique ID; all information required to reconstruct the experiment can be found on the CommonRoad website. Each benchmark is composed by a vehicle model, a cost function, and a scenario (including goals and constraints). The scenarios are partly recorded from real traffic and partly hand-crafted to create dangerous situations. We hope that CommonRoad saves researchers time since one does not have to search for realistic parameters of vehicle dynamics or realistic traffic situations, yet provides the freedom to compose a benchmark that fits one's needs.

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SPOT: A tool for set-based prediction of traffic participants

Cited by 69 publications

References 21 publications

Enhancing motion safety by identifying safety-critical passageways

Enhancing motion safety by identifying safety-critical passageways

Tackling Occlusions & Limited Sensor Range with Set-based Safety Verification

CommonRoad: Composable benchmarks for motion planning on roads

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