Automated driving: Safe motion planning using positively invariant sets

2017 ◽  
Author(s):  
Karl Berntorp ◽  
Avishai Weiss ◽  
Claus Danielson ◽  
Ilya V. Kolmanovsky ◽  
Stefano Di Cairano
Keyword(s):  
Motion Planning ◽  
Invariant Sets ◽  
Automated Driving ◽  
Positively Invariant Sets

Online feedback motion planning for spacecraft obstacle avoidance using positively invariant sets

2020 ◽  
Vol 65 (10) ◽  
pp. 2424-2434
Author(s):  
Dengwei Gao ◽  
Jianjun Luo ◽  
Weihua Ma ◽  
Brendan Englot
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Invariant Sets ◽  
Online Feedback ◽  
Positively Invariant Sets

scholarly journals Transient stability analysis of power grids with admissible and maximal robust positively invariant sets

2020 ◽  
Vol 68 (12) ◽  
pp. 1011-1021
Author(s):  
Tim Aschenbruck ◽  
Willem Esterhuizen ◽  
Stefan Streif
Keyword(s):  
Power Systems ◽  
Transient Stability ◽  
Energy Transition ◽  
Power Grids ◽  
Invariant Sets ◽  
Admissible Sets ◽  
Major Fault ◽  
Bounded Disturbances ◽  
One Machine ◽  
Positively Invariant Sets

AbstractThe energy transition is causing many stability-related challenges for power systems. Transient stability refers to the ability of a power grid’s bus angles to retain synchronism after the occurrence of a major fault. In this paper a set-based approach is presented to assess the transient stability of power systems. The approach is based on the theory of barriers, to obtain an exact description of the boundaries of admissible sets and maximal robust positively invariant sets, respectively. We decompose a power system into generator and load components, replace couplings with bounded disturbances and obtain the sets for each component separately. From this we deduce transient stability properties for the entire system. We demonstrate the results of our approach through an example of one machine connected to one load and a multi-machine system.

scholarly journals A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

2021 ◽  
Vol 2 ◽  
Author(s):  
Mysore Narasimhamurthy Sharath ◽  
Babak Mehran
Keyword(s):  
Motion Planning ◽  
Performance Indicators ◽  
Performance Metrics ◽  
Recent Literature ◽  
Automated Driving ◽  
Road Vehicles ◽  
Multiple Stimulus ◽  
Human Likeness ◽  
Environment Perception ◽  
The Ideal

The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.

Positively invariant sets of a T1DM model: Hypoglycemia prediction and avoidance

2019 ◽  
Vol 356 (11) ◽  
pp. 5652-5674
Author(s):  
T. MohammadRidha ◽  
P.S. Rivadeneira ◽  
N. Magdelaine ◽  
M. Cardelli ◽  
C.H. Moog
Keyword(s):  
Invariant Sets ◽  
Positively Invariant Sets
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