Sampling-Based Threat Assessment Algorithms for Intersection Collisions Involving Errant Drivers

Aoude, Georges S.; Luders, Brandon D.; How, Jonathan P.; Pilutti, Tom E.

doi:10.3182/20100906-3-it-2019.00100

Cited by 17 publications

(14 citation statements)

References 12 publications

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“…In the worst-case approach, the dynamic obstacle is assumed to be actively trying to collide with the planning agent, or "host vehicle" (Miloh and Sharma (1976); Lachner (1997)). The predicted trajectory of the dynamic obstacle is the solution of a differential game, where the dynamic obstacle is modeled as a pursuer and the host vehicle as an evader (Aoude et al (2010a)). Despite providing a lower bound on safety, such solutions are inherently conservative, and thus limited to short time horizons in collision warning/mitigation problems to keep the level of false positives below a reasonable threshold (Kuchar and Yang (2002)).…”

Section: Related Workmentioning

confidence: 99%

Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

et al. 2013

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This paper presents a real-time path planning algorithm that guarantees probabilistic feasibility for autonomous robots with uncertain dynamics operating amidst one or more dynamic obstacles with uncertain motion patterns. Planning safe trajectories under such conditions requires both accurate prediction and proper integration of future obstacle behavior within the planner. Given that available observation data is limited, the motion model must provide generalizable predictions that satisfy dynamic and environmental constraints, a limitation of existing approaches. This work presents a novel solution, named RR-GP, which builds a learned motion pattern model by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based reachability computation. Obstacle trajectory GP predictions are conditioned on dynamically feasible paths identified from the reachability analysis, yielding more accurate predictions of future behavior. RR-GP predictions are integrated with a robust path planner, using chance-constrained RRT, to identify probabilistically feasible paths. Theoretical guarantees of probabilistic feasibility are shown for linear systems under Gaussian uncertainty; approximations for nonlinear dynamics and/or non-Gaussian uncertainty are also presented. Simulations demonstrate that, with this planner, an autonomous vehicle can safely navigate a complex environment in realtime while significantly reducing the risk of collisions with dynamic obstacles.

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Section: Related Workmentioning

confidence: 99%

Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

et al. 2013

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“…The Gaussian process is applied to predict the DT motion patterns to guide the sampling process in the pursuit mode. The DT avoidance problem is modeled as a pursuit-evasion game in the zero-sum differential game theory in [34]. The works' background is the intelligent transportation.…”

Section: Threat Assessmentmentioning

confidence: 99%

Online UAV path planning in uncertain and hostile environments

Wen

et al. 2015

Int. J. Mach. Learn. & Cyber.

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Taking uncertainties of threats and vehicles' motions and observations into account, the challenge we have to face is how to plan a safe path online in uncertain and dynamic environments. We construct the static threat (ST) model based on an intuitionistic fuzzy set (A-IFS) to deal with the uncertainty of a environmental threat. The problem of avoiding a dynamic threat (DT) is formulated as a pursuit-evasion game. A reachability set (RS) estimator of an uncertain DT is constructed by combining the motion prediction with a RRT-based method. An online path planning framework is proposed by integrating a sub goal selector, a sub tasks allocator and a local path planner. The selector and allocator are presented to accelerate the path searching process. Dynamic domain rapidly-exploring random tree (DDRRT) is combined with the linear quadratic Gaussian motion planning (LQG-MP) method when searching local paths under threats and uncertainties. The path that has been searched is further improved by using a safety adjustment method and the RRT* method in the planning system. The results of Mont Carlo simulations indicate that the proposed algorithm behaves well in planning safe paths online in uncertain and hostile environments.

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“…Its ability to handle model uncertainty has also been studied [10,15]. However, we take an alternate approach based on the observation that CL-RRT explores the reachable space for a dynamic system [16]. By introducing disturbance estimates in the forward-simulation process, we generate a mapping between the reference space of the closed-loop system and its reachable space in the presence of external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical adaptive planning in environments with uncertain, spatially-varying disturbance forces

Desaraju

Michael

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents a hierarchical planning architecture that generates vehicle trajectories that adapt to uncertain, spatially-varying disturbance forces toward enhanced tracking performance. The disturbance force is modeled as a discrete conditional probability distribution that is updated online by local measurements as the vehicle navigates. A global planner identifies the optimal route to the goal and adapts this route according to a cost metric derived from the belief distribution on the disturbance force. A local planner embeds the belief distribution in the trajectory generation process to compute dynamically feasible trajectories along the global plan that evolve with the belief. Simulation studies analyze and demonstrate the increased trajectory tracking accuracy via the proposed methodology with a single vehicle and the impact of the approach to multiple agents performing collaborative inference toward enhanced collective performance.

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Sampling-Based Threat Assessment Algorithms for Intersection Collisions Involving Errant Drivers

Cited by 17 publications

References 12 publications

Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

Online UAV path planning in uncertain and hostile environments

Hierarchical adaptive planning in environments with uncertain, spatially-varying disturbance forces

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