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Sistem automat de asistenta auto Drive!

Obiective

Dezvoltarea unui sistem automat de asistenta a conducatorilor auto

Ideea de baza

Unora dintre conducatorii auto le-ar prinde bine putin ajutor in trafic in activitati precum: detectia obstacolelor, adaptarea vitezei la conditiile meteo si rutiere, navigarea pe harta. Pentru aceasta este nevoie de o aplicatie inteligenta care, pe baza informatiilor curente captate din trafic si anumiti senzori (de ex GPS) sa recomande conducatorilor auto anumite actiuni.

TO DO list

  1. Recunoasterea
  • obstacolelor (pietoni, marcaje, alte masini, etc.)
  • semnelor de circulatie si a marcajelor in trafic
  • conditiilor meteo si rutiere curente
  1. Identificarea unui traseu optim (intre 2 locatii)
  2. Nota:
  • In cazul recunoasterii unor anumite elemente, se vor antrena si valida modele de clasificare folosind informatii preluate din seturi de date de tip benchmark, iar clasificatorii rezultati vor fi testati folosind informatii noi preluate de diferiti senzori atasati autovehicului.
  • In cazul identificarii unui anumit traseu, se vor folosi informatii din harti (sub forma unor grafe) si algoritmi de optimizare corespunzatori.
  1. Imbunatatire componente inteligente
  • Din perspectiva calitatii procesului de invatare automata
  • Din perspectiva complexitatii temporale si spatiale aferenta clasificatorului
  • Din perspectiva clientului prin considerarea si anticiparea comportamentului celorlalti participanti la trafic (pietoni, autovehicule) si prin scalarea sistemului automat in cazul unui grup de autovehicule (de ex reteaua de taxi-uri a unui oras)

Date si referinte

Pedestrians

  1. http://www.pedestrian-detection.com/
  2. Franke, Uwe, et al. "From door to door—Principles and applications of computer vision for driver assistant systems." Intelligent Vehicle Technologies. Butterworth-Heinemann, 2001. 131-188.
  3. Geronimo, David, et al. "Survey of pedestrian detection for advanced driver assistance systems." IEEE Transactions on Pattern Analysis & Machine Intelligence 7 (2009): 1239-1258.
  4. Dollar, Piotr, et al. "Pedestrian detection: An evaluation of the state of the art." IEEE transactions on pattern analysis and machine intelligence 34.4 (2011): 743-761.
  5. Paden, Brian, et al. "A survey of motion planning and control techniques for self-driving urban vehicles." IEEE Transactions on intelligent vehicles 1.1 (2016): 33-55.
  6. Rasouli, Amir, and John K. Tsotsos. "Autonomous vehicles that interact with pedestrians: A survey of theory and practice." IEEE Transactions on Intelligent Transportation Systems (2019).
  7. Rasouli, Amir, Iuliia Kotseruba, and John K. Tsotsos. "Understanding pedestrian behavior in complex traffic scenes." IEEE Transactions on Intelligent Vehicles 3.1 (2017): 61-70.

Routing

  1. http://neo.lcc.uma.es/vrp/vehicle-routing-problem/
  2. Pillac, Victor, et al. "A review of dynamic vehicle routing problems." European Journal of Operational Research 225.1 (2013): 1-11.
  3. Braekers, Kris, Katrien Ramaekers, and Inneke Van Nieuwenhuyse. "The vehicle routing problem: State of the art classification and review." Computers & Industrial Engineering 99 (2016): 300-313.
  4. Goksal, Fatma Pinar, Ismail Karaoglan, and Fulya Altiparmak. "A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery." Computers & Industrial Engineering 65.1 (2013): 39-53.
  5. Marinakis, Yannis, and Magdalene Marinaki. "A hybrid genetic–Particle Swarm Optimization Algorithm for the vehicle routing problem." Expert Systems with Applications 37.2 (2010): 1446-1455.

Imagini si alte date

  1. INRIA http://pascal.inrialpes.fr/data/human/
  2. Daimler http://www.lookingatpeople.com/download-daimler-stereo-ped-det-benchmark/index.html
  3. Caltech http://vision.caltech.edu/Image_Datasets/CaltechPedestrians/index.html
  4. JAAD http://data.nvision2.eecs.yorku.ca/JAAD_dataset/