A platoon of vehicles approaching an intersection: A testing platform for safe intersections

Diab, Hilal; Makhlouf, Ibtissem Ben; Kowalewski, Stefan

doi:10.1109/itsc.2012.6338822

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…While efforts with physical test fleets are steadily increased [3]- [5], it is obvious that physical tests alone are too costly and time consuming. For prototyping new ideas more quickly, scaled-down vehicles are sometimes used [6], [7]. The main direction taken by industry and academia, however, is to accompany physical tests with virtual driving tests, requiring the development of a simulation environment in which vehicles can be tested by many factors faster than in real-time, in order to expose the vehicle under test-in this work referred to as the ego vehicle-to many different situations, see e.g., [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of Safety-Critical Test Scenarios for Collision Avoidance of Road Vehicles

Althoff

Lutz

2018

2018 IEEE Intelligent Vehicles Symposium (IV)

104

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It is apparent that one cannot rely solely on physical test drives for ensuring the correct functionality of autonomous vehicles. Since physical test drives are costly and time consuming, it is advantageous to accompany them with computer simulations. However, since most traffic scenarios are not challenging, even simulations are often too time consuming. To address this issue, we present an approach that creates automatically critical driving situations, i.e., situations with a small solution space for avoiding a collision. Our approach combines reachability analysis for determining the size of the solution space with optimization techniques to shrink it. The solution space is reduced by shifting the initial states of traffic participants, demanding an immediate and correct action of the vehicle under test. We demonstrate our approach by automatically increasing the criticality of several initially uncritical situations recorded from real traffic.

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Section: Introductionmentioning

confidence: 99%

Automatic Generation of Safety-Critical Test Scenarios for Collision Avoidance of Road Vehicles

Althoff

Lutz

2018

2018 IEEE Intelligent Vehicles Symposium (IV)

104

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Platoon Driving Intelligence. A Survey

Santana

Medina

Rubio-Royo

2015

Computer Aided Systems Theory – EUROCAST 2015

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Platoon-based autonomous vehicle speed optimization near signalized intersections

Faraj

Sancar

Fi̇dan

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Road congestion in urban environments, especially near signalized intersections, has been a major cause of significant fuel and time waste. Various solutions have been proposed to solve the problem of increasing idling times and number of stops of vehicles at signalized intersections, ranging from infrastructure to vehiclebased techniques. However, all the solutions introduced to solve the problem have approached the problem from a single vehicle point of view. This research introduces a game-theoretic cooperative speed optimization framework to minimize vehicles' idling times and number of stops at signalized intersections. This framework consists of three modules to cover individual autonomous vehicle speed optimization; conflict recognition; and cooperative speed decision making. A time token allocation algorithm is introduced through the proposed framework to allow the vehicles to cooperate and agree on certain speed actions such that the average idling times and number of stops are minimized. Simulation to test and validate the proposed framework is conducted and results are reported.

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A platoon of vehicles approaching an intersection: A testing platform for safe intersections

Cited by 4 publications

References 11 publications

Automatic Generation of Safety-Critical Test Scenarios for Collision Avoidance of Road Vehicles

Automatic Generation of Safety-Critical Test Scenarios for Collision Avoidance of Road Vehicles

Platoon Driving Intelligence. A Survey

Platoon-based autonomous vehicle speed optimization near signalized intersections

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