Not-at-Fault Driving in Traffic: A Reachability-Based Approach

Vaskov, Sean; Larson, Hannah; Kousik, Shreyas; Johnson-Roberson, Matthew; Vasudevan, Ram

doi:10.1109/itsc.2019.8917052

Cited by 20 publications

(8 citation statements)

References 7 publications

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“…Based on this principle, several mature toolboxes have been developed [23,24,3] and applied to many practical engineering problems, such as flight control systems [25,26,27,7,28], ground traffic management systems [29,30,31], air traffic management systems [2,3], etc. Denote the number of grid points in the ith dimension of the Cartesian grid as N i , then the storage space consumed to save R(K, T ) is proportional to n i=1 N i .…”

Section: Level Set Methodsmentioning

confidence: 99%

Computation of Reachable Sets Based on Hamilton-Jacobi-Bellman Equation with Running Cost Function

Liao¹,

Tao²

2021

Preprint

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A novel method for computing reachable sets is proposed in this paper. In the proposed method, a Hamilton-Jacobi-Bellman equation with running cost function is numerically solved and the reachable sets of different time horizons are characterized by a family of non-zero level sets of the solution of the Hamilton-Jacobi-Bellman equation. In addition to the classical reachable set, by setting different running cost functions and terminal conditions of the Hamilton-Jacobi-Bellman equation, the proposed method allows to compute more generalized reachable sets, which are referred to as cost-limited reachable sets. In order to overcome the difficulty of solving the Hamilton-Jacobi-Bellman equation caused by the discontinuity of the solution, a method based on recursion and grid interpolation is employed. At the end of this paper, some examples are taken to illustrate the validity and generality of the proposed method.

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Section: Level Set Methodsmentioning

confidence: 99%

Computation of Reachable Sets Based on Hamilton-Jacobi-Bellman Equation with Running Cost Function

Liao¹,

Tao²

2021

Preprint

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“…Form of Reachability Analysis Type of Guarantee [87], [88] maxFRS + minBRS guarantees collision if no solution for "anticipated reachable set" is found [89] FRS of controlled dynamics predicts unavoidable collision within control policy [90] overapproximated maxFRS, maxFRT all possible forward motions are included [81] inaccuracy around trajectory-planned vehicle checks probability of collision within the inaccuracy model bounds [91] minBRT guarantees collision free in horizon [72] maxBRS for partially-controlled & uncontrolled vehicle conservative collision-free guarantee for non-extreme driving [92] overapproximated maxFRI guarantee "not-at-fault" safety by checking potential collision in time intervals leading to end of time horizon [93] maxBRT guarantees safe interoperability: possibly safe human takeover from autonomous driving system [94] maxFRS of uncontrolled dynamics guarantees collision free in horizon [95] funnel (a variant of FRT for controlled vehicle) guarantees collision-free if a funnel can be found to stay clear of obstacles at all times [96], [97], [98] maxFRS implemented by Flow* [99] guarantees safety of deep neural network (DNN) controlled close-loop system [100] maxFRS of (possibly) occluded vehicle guarantees collision-free with possibly occluded vehicle(s) [101] maxFRS of each agent communicated through a decentralized network real-time collision-free guarantee for a group of autonomous agents [102] maxFRS + collision state pruning + maxBRS guarantees collision-free and successful reach of target state if "controller contract" (state constraint tubes) are honored [86] maxFRT + pedestrian intent prediction guarantees safety within a high probability bound of pedestrian motion collision frequency [53], a vital quantity in determining automotive safety integritity level (ASIL) in ISO26262 [48]. If the vehicle is indeed in such a state, an impact preparation should be executed (usually with extensive use of braking to slow down [104], or by following not-at-fault trajectories [92].) since collision is inevitable.…”

Section: Referencementioning

confidence: 99%

Formal Certification Methods for Automated Vehicle Safety Assessment

Zhao,

Yurtsever,

Paulson

et al. 2022

Preprint

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Challenges related to automated driving are no longer focused on just the construction of such automated vehicles (AVs), but in assuring the safety of their operation. Recent advances in Level 3 and Level 4 autonomous driving have motivated more extensive study in safety guarantees of complicated AV maneuvers, which aligns with the goal of ISO 21448 (Safety of the Intended Functions, or SOTIF), i.e. minimizing unsafe scenarios both known and unknown, as well as Vision Zero -eliminating highway fatalities by 2050. A majority of approaches used in providing safety guarantees for AV motion control originate from formal methods, especially reachability analysis (RA), which relies on mathematical models for the dynamic evolution of the system to provide guarantees. However, to the best of the authors' knowledge, there have been no review papers dedicated to describing and interpreting state-of-the-art of formal methods in the context of AVs. In this work, we provide both an overview of the safety verification, validation and certification process, as well as review formal safety techniques that are best suited to AV applications. We also propose a unified scenario coverage framework that can provide either a formal or sample-based estimate of safety verification for full AVs. Finally, remaining challenges and future opportunities beyond the scope of current published research for assured AV safety are presented.

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“…Note that it is reasonable to satisfy (12) in practice, as validated empirically in both simulation and on hardware for a variety of robot morphologies [12], [13], [17], [18], [24], [28], [29]. Moreover, if one is unsure if ( 12) is satisfied, one can simply use larger Z (i, j,h) err .…”

Section: A Justifying Ers Conservatismmentioning

confidence: 99%

Reachability-based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Control

Shao¹,

Chen²,

Kousik³

et al. 2020

Preprint

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Reinforcement Learning (RL) algorithms have achieved remarkable performance in decision making and control tasks due to their ability to reason about long-term, cumulative reward using trial and error. However, during RL training, applying this trial-and-error approach to real-world robots operating in safety critical environment may lead to collisions. To address this challenge, this paper proposes a Reachability-based Trajectory Safeguard (RTS), which leverages trajectory parameterization and reachability analysis to ensure safety while a policy is being learned. This method ensures a robot with continuous action space can be trained from scratch safely in real-time. Importantly, this safety layer can still be applied after a policy has been learned. The efficacy of this method is illustrated on three nonlinear robot models, including a 12-D quadrotor drone, in simulation. By ensuring safety with RTS, this paper demonstrates that the proposed algorithm is not only safe, but can achieve a higher reward in a considerably shorter training time when compared to a non-safe counterpart.

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Not-at-Fault Driving in Traffic: A Reachability-Based Approach

Cited by 20 publications

References 7 publications

Computation of Reachable Sets Based on Hamilton-Jacobi-Bellman Equation with Running Cost Function

Computation of Reachable Sets Based on Hamilton-Jacobi-Bellman Equation with Running Cost Function

Formal Certification Methods for Automated Vehicle Safety Assessment

Reachability-based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Control

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