Path Planning with Discrete Geometric Shape Patterns

Simon, Bruno; Riegl, Peter; Gaull, Andreas

doi:10.1109/icves.2019.8906449

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…A geometric technique based on discrete shape patterns built by assembling circular arcs, line segments and clothoids was proposed in [36] for collision avoidance in real driving scenarios. The authors aimed to generate robust and rapid trajectories by discretizing continuous trajectories to polygonal chains via the deflection of their edges.…”

Section: ) Path Planning Before Speed Planning Approachesmentioning

confidence: 99%

“…An MPC-based approach for motion planning in overtake scenarios was presented in [24]. The authors combined reachable sets, to iteratively generate reference targets based on the current maneuver, together Interpolating curves [33], [89], [34], [35], [36], [37] Interpolation of quintic Bézier curves for trajectory planning in urban environments; interpolation of consecutive quartic Bézier curves optimizing consecutive curves for generation smooth trajectories; B-spline curves for the interpolation of centerline of reference lane for candidate paths generation. Cubic polynomial splines for speed profile generation; Clothoids based path tentacles in dynamic environments for obstacle avoidance; assembling of circular arcs, line segments and clothoids for collision avoidance; RIS combined with Bézier curves for path generation in semiconstrained highly dynamic environments [33], [34], [35], [36], [37] [35]…”

Section: Stability Level (Control)mentioning

confidence: 99%

“…[33], [34], [35], [36], [37] [35], [36], [37] Graphsearch based [49], [38] State lattices based discretization combined with splines for the paths set generation for fast real-time planning in semi-structured environments; A* graph-search combined with RRT for the navigation of an AV through an unmapped road scenario [38] [38] [38] [49], [38] Samplingbased [35], [48], [39], [16] Evidential occupancy grid for modeling the environment and allowing to represent the uncertainty, combined with clothoid tentacles; navigation through clothoid tentacles selection with a high-level maneuver planner for the obstacle avoidance application; occupancy grid discretization with optimization based path generation; sampling-based motion planner with tactical maneuver discovery reasoning [39], [16] [35] [35], [48] [35], [48], [39], [16] Optimizationbased [40], [41], [50], [42] Path and speed profile generation by iterative optimization, combining dynamic programming with quadratic splines; convex optimization for optimal speed profile generation for static and dynamic environments; speed profile generation based on optimal control; MPC-based simultaneous path and speed planning for obstacle avoidance scenarios [40], [41], [42] [42]…”

Section: Stability Level (Control)mentioning

confidence: 99%

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Review of Decision-Making and Planning Approaches in Automated Driving

Garrido

Resende

2022

IEEE Access

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The number of research papers on decision-making systems in automated driving has increased significantly over the last few years. Decision-making for automated driving can be performed at different levels: (i) strategic level: generating the optimal route up to the destination; (ii) tactical level: identifying and ranking feasible high-level maneuvers that the vehicle can perform, considering the dynamic objects that are in the surroundings; (iii) operational level: generating a collision-free trajectory (path and speed profile) up to the planning horizon; (iv) stability level: computing the motion control commands for tracking the trajectory. Additionally, supervision can be understood as a combination of one or more decision-making levels. Previous reviews have focused either on one of the levels of decision-making or on a specific environment where the approaches are applied, without any distinction between the contexts in which they are applied (robotics, unmanned vehicles or automated driving). This review studies the state-of-the-art on the decision-making approaches applied specifically to automated driving, during the last lustrum.

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Section: ) Path Planning Before Speed Planning Approachesmentioning

confidence: 99%

Section: Stability Level (Control)mentioning

confidence: 99%

Section: Stability Level (Control)mentioning

confidence: 99%

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Review of Decision-Making and Planning Approaches in Automated Driving

Garrido

Resende

2022

IEEE Access

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Criticality Measures to Evaluate the Triggering Decision of Collision Avoidance Functions at Intersections

Riegl¹,

Gaull²,

Beitelschmidt³

2020

jtle

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In order to significantly reduce the amount of testing required to validate the behavior of vehicle safety functions with real vehicles, the scenarios in the simulation must reproduce the traffic as realistically as possible. For this reason, a microscopic traffic flow simulation is used. Since the road users show a perfect behavior by default, driving errors have to be induced. The necessary adjustments in the existing driver models are presented schematically in this paper. These changes in the driving behavior ensure that some road users cause situations that are critical for others. Such scenarios are required as input data for testing vehicle safety functions in a vehicle dynamics simulation. A decisive factor for the quality with which a critical situation can be successfully managed by a vehicle safety function is the trigger time. For this purpose, approaches are presented for estimating the period in which a collision can still be avoided by braking or evasive maneuver. Three intersection scenarios illustrate the functioning of these criteria.

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