Efficient Computation of Invariably Safe States for Motion Planning of Self-Driving Vehicles

Pek, Christian; Althoff, Matthias

doi:10.1109/iros.2018.8593597

Cited by 28 publications

(44 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The invariant sets are generated exploiting techniques from reachability analysis and considering an infinite time horizon, unlike most of the other methods based on reachability. In [21], invariant safe sets are computed for self-driving automobiles in linear time and with provable formal safety guarantees.…”

Section: Safety Analysis Based On Reachabilitymentioning

confidence: 99%

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Miraglia

Hook

2021

IEEE Access

View full text Add to dashboard Cite

New generations of small advanced aircraft may soon transform how people and freight are transported. Thus, ensuring safe and reliable operation of these systems, in all plausible scenarios, is of utmost priority. In this paper, we address this problem by proposing a method to verify the safety of an autonomous aircraft controller following a navigational policy. The policy is encoded using discrete vector fields that are meant to drive the vehicle to a goal state and away from obstacles or restricted airspace. The solution presented in this paper provides the ability to verify the policy, catching unsafe scenarios which may be missed by random Monte Carlo methods or general reachability analysis. After illustrating the main theoretical principles, a possible practical implementation is described and compared with analysis based on Monte Carlo simulations. The comparison shows an insightful example where Monte Carlo simulations fail to detect several corner cases that are uncovered by the proposed method. In the conclusion of the paper, we provide insights about possible future research to implement the proposed solution obtaining higher accuracy and efficiency. INDEX TERMS Navigation, Unmanned aerial vehicles, Autonomous agents, Reachability analysis VOLUME 4, 2016 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3062662, IEEE Access Giovanni Miraglia et al.: Geometric Verification of Vector Fields for Autonomous Aircraft Navigation Giovanni Miraglia et al.: Geometric Verification of Vector Fields for Autonomous Aircraft Navigation LOYD R. HOOK (Member, IEEE) has worked in aircraft autonomous systems research and implementation for the past 15 years beginning at NASA Dryden/Armstrong flight research center and continuing at his current position as Assistant Professor at the University of Tulsa. Loyd is currently the director of the TU Vehicle Autonomy and Intelligence Lab (TU VAIL) where his research focus is the development of safety assured autonomous vehicle systems. Loyd has MS and Ph.D. degrees from the University of Oklahoma in Electrical and Computer Engineering from 2006 and 2011 respectively.

show abstract

Section: Safety Analysis Based On Reachabilitymentioning

confidence: 99%

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Miraglia

Hook

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Although it is collision-free at the current time t, it may eventually collide with the obstacle at time t + t , t > 0. To formally ensure its safety, we, therefore, require the vehicle to be in an invariably safe state [1]. An invariably safe state is defined recursively: a state is deemed invariably safe if a collision-free trajectory ending at another invariably safe state exists.…”

Section: Invariably Safe Setsmentioning

confidence: 99%

“…Invariably Safe Sets (ISSs) [1] are sets of states which ensure that autonomous vehicles can remain safe for an infinite time horizon. If the ISS of a vehicle is used as its target set for motion planning, one can always find motions that do not cause collisions for an infinite time horizon.…”

Section: Introductionmentioning

confidence: 99%

Provably-Safe Cooperative Driving via Invariably Safe Sets

Liu

Pek

Althoff

2020

2020 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

View full text Add to dashboard Cite

We address the problem of provably-safe cooperative driving for a group of vehicles that operate in mixed traffic scenarios, where both autonomous and human-driven vehicles are present. Our method is based on Invariably Safe Sets (ISSs), which are sets of states that let each of the cooperative vehicles remain safe for an infinite time horizon. The potential conflicts between the ISSs of a group of cooperative vehicles are resolved by examining and negotiating their Safe Maneuver Corridors. As a result, each vehicle obtains its negotiated ISS, which is used as target sets for motion planning. We demonstrate the applicability and benefits of our method on various traffic scenarios from the CommonRoad benchmark suite.

show abstract

“…To guarantee safety for an infinite time horizon, the planned motion of the ego vehicle must end in a state that is safe forever. Such invariably safe states can be determined using our set-based prediction [13].…”

Section: Introductionmentioning

confidence: 99%

Set-Based Prediction of Traffic Participants Considering Occlusions and Traffic Rules

Koschi

Althoff

2021

IEEE Trans. Intell. Veh.

Self Cite

View full text Add to dashboard Cite

Provably safe motion planning for automated road vehicles must ensure that planned motions do not result in a collision with other traffic participants. This is a major challenge in autonomous driving, since the future behavior of other traffic participants is not known and since traffic participants are often hidden due to occlusions. In this work, we propose a formal setbased prediction that contains all acceptable future behaviors of both detected and potentially hidden traffic participants. Based on formalized traffic rules and nondeterministic motion models, we perform reachability analysis to predict the set of possible occupancies and velocities of vehicles, pedestrians, and cyclists. Real-world experiments with a test vehicle in various traffic situations demonstrate the applicability and real-time capability of our over-approximative prediction for both online verification and fail-safe trajectory planning. Even in congested, complex traffic scenarios, our forecasting approach enables self-driving vehicles to never cause accidents.

show abstract

Efficient Computation of Invariably Safe States for Motion Planning of Self-Driving Vehicles

Cited by 28 publications

References 35 publications

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Provably-Safe Cooperative Driving via Invariably Safe Sets

Set-Based Prediction of Traffic Participants Considering Occlusions and Traffic Rules

Contact Info

Product

Resources

About