Machine Learning-Assisted Cognition of Driving Context and Avoidance of Road Obstacles

  • Manolo Dulva HinaEmail author
  • Andrea Ortalda
  • Assia Soukane
  • Amar Ramdane-Cherif
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1222)


In the vehicle of the future, an intelligent vehicle should be able to recognize the driving context so as it would be able to perform the necessary actions to continue the trip up to its intended destination. Moreover, such intelligent vehicle should also be able to detect and recognize road obstacles, as it is the failure to recognize an obstacle and avoid it that often lead to road accident, normally causing human fatalities. In this paper, knowledge engineering related to the cognitive processes of driving context detection, perception, decision and optimal action related to the driving context and avoiding road obstacles. Ontology and formal specifications are used to describe such mechanism. Different supervised learning algorithms are used for cognition of driving context and in recognizing and classifying obstacles. The avoidance of obstacles is implemented using reinforcement learning. The work is validated using driving simulator in the laboratory. This work is a contribution to the ongoing research in safe driving, and the application of machine learning leading to prevention of road accidents.


Ontology Formal specification Machine learning Safe driving Smart vehicle Cognitive informatics 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Manolo Dulva Hina
    • 1
    Email author
  • Andrea Ortalda
    • 1
  • Assia Soukane
    • 1
  • Amar Ramdane-Cherif
    • 2
  1. 1.ECE Paris School of EngineeringParisFrance
  2. 2.Université de Versailles Paris-SaclayVelizyFrance

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