Phd Thesis (M/F): Probabilistic Risk Assessment And Management Architecture For Safe Autonomous...

Universities and Institutes of France
September 26, 2022
Contact:N/A
Offerd Salary:Negotiation
Location:N/A
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Contract Type:Temporary
Working Time:Full time
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Job Ref.:N/A
  • Organisation/Company: CNRS
  • Research Field: Computer science Engineering Mathematics
  • Researcher Profile: First Stage Researcher (R1)
  • Application Deadline: 26/09/2022 23:59 - Europe/Brussels
  • Location: France › COMPIEGNE
  • Type Of Contract: Temporary
  • Job Status: Full-time
  • Hours Per Week: 35
  • Offer Starting Date: 01/10/2022
  • This thesis subject will be carried out in close collaboration between the SyRI team of Heudiasyc and the ACENTAURI project team of INRIA Sophia Antipolis and this within the framework of the ANR ANNAPOLIS project.

    The proposed thesis subject aims to make the decision-making of intelligent vehicles (IV) even more robust and safe during the complete delegation of driving phases, and this in highly dynamic and constrained urban environments. In this context, the VI can face multiple visual occlusions and unpredictable behaviors of the moving entities around. The main objective of this thesis is to propose a global probabilistic multi- controller architecture (P-MCA) with a reliable risk assessment and management system. The main challenges are to have the safest movement of the VIs, even in complex environments/situations, but also to ensure the fluidity of the trajectories of these vehicles (thereby guaranteeing the comfort of the passengers). To achieve these objectives, mainly inspired by the work given in Adouane 17, Iberraken 18 a new global Probabilistic Multi- Controller Architecture, adapted to navigation in urban areas, must be developed and embedded in each VI. This architecture also aims to jointly use adaptive and model-predictive controls (based on both steering/braking capabilities and the desired response of the VI in the short to medium term) to generate safe trajectories even in large diversity of driving conditions, uncertain and unexpected events/situations. These modifications will inevitably lead to proposing an appropriate stochastic control law with robust properties Dahmane 18, such as the one based on the stochastic MPC (Model Predictive Control) Mesbab016. MPC uses models to predict future developments within a particular time horizon. A promising approach is also to explore the potentialities of the Model Predictive Path Integral (MPPI) Williams 16, which is a sample-based MPC that shows good results in autonomous navigation under difficult conditions Philippe 19. Assessing and managing vehicle risk is an important part of the intended overall control architecture. In the literature, many methods have been used as decision methods Schubert 12. Probabilistic decision making aims to make the best continuous decisions in constrained and uncertain environments. To do this, a robust and operational Markov decision-making process based on the Multi-level Bayesian Decision Making Network (MB-DMN), as illustrated in Iberraken 19b, will be developed. This type of process makes it possible to have reliable retrospections on the consequence of the actions taken by the VI according to the expected trajectories of the surrounding entities. Its purpose is to minimize the risk of collisions of the VI when it is confronted with dangerous and/or unexpected situations. The extension of the MB-DMN together with appropriate augmented perception and better metrics to characterize the probabilistic behaviors and trajectories of surrounding entities will enhance the reliability of the targeted decision process.

    References: Adouane 17, L. Adouane, Reactive versus cognitive vehicle navigation based on optimal local and global PELC. Robotics and Autonomous Systems (RAS), , volume 88, pp. 51–70, February 2017, DOI 10.1016/j.robot.2016.11.006 Iberraken 19a, D. Iberraken, L. Adouane and D.Dieumet, "Multi-Controller Architecture for Reliable Autonomous Vehicle Navigation: Combination of Model- Driven and Data-Driven Formalization", Workshop FRCA-IAV, IEEE 2019 IEEE Intelligent Vehicles Symposium. Iberraken 19b, D. Iberraken, L. Adouane, and D. Dieumet, "Reliable Risk Management for Autonomous Vehicles based on Sequential Bayesian Decision Networks and Dynamic Inter-Vehicular Assessment", IEEE 2019 IEEE Intelligent Vehicles Symposium. Ben-Lakhal 19, N.M Ben-Lakhal, L. Adouane, O. Nasri, and J. Ben Hadj Slama, Risk Management for Intelligent Vehicles based on interval analysis of TTC, 10th IFAC Symposium on Intelligent Autonomous Vehicles (IAV'19), 3-5 July 2019, Gdansk-Poland. Maurer 16, Maurer, M., Gerdes, J.C., Lenz, B., Winner, H, .Autonomous Driving: Technical, Legal and Social Aspects, Springer, 978-3-662-48845-4, 2016. Philippe 19, C. Philippe, L. Adouane, B. Thuilot, A. Tsourdos and H-S. Shin, Risk and Comfort Management for Multi-Vehicle Navigation using a Flexible and Robust Cascade Control Architecture, European Conf. on Mobile Robotics, Paris-France, 2017. Schubert 12, Schubert, R. Evaluating the utility of driving: Toward automated decision making under uncertainty. IEEE Transactions on Intelligent Transportation Systems, 13(1), 354-364, 2012. Mesbab016, A. Mesbah, "Stochastic Model Predictive Control: An Overview and Perspectives for Future Research," in IEEE Control Systems Magazine, vol. 36, no. 6, pp. 30-44, Dec. 2016, doi: 10.1109/MCS.2016.2602087. Williams2016, G. Williams, P. Drews, B. Goldfain, J. M. Rehg and E. A. Theodorou, "Aggressive driving with model predictive path integral control," 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 1433-1440, doi: 10.1109/ICRA.2016.7487277.

    Web site for additional job details

    https: // emploi.cnrs.fr/Offres/Doctorant/UMR7253-LOUADO-001/Default.aspx

    Required Research Experiences
  • RESEARCH FIELD
  • Engineering

  • YEARS OF RESEARCH EXPERIENCE
  • None

  • RESEARCH FIELD
  • Computer science

  • YEARS OF RESEARCH EXPERIENCE
  • None

  • RESEARCH FIELD
  • Mathematics

  • YEARS OF RESEARCH EXPERIENCE
  • None

    Offer Requirements
  • REQUIRED EDUCATION LEVEL
  • Engineering: Master Degree or equivalent

    Computer science: Master Degree or equivalent

    Mathematics: Master Degree or equivalent

  • REQUIRED LANGUAGES
  • FRENCH: Basic

    Contact Information
  • Organisation/Company: CNRS
  • Department: Heuristique et Diagnostic des Systèmes Complexes
  • Organisation Type: Public Research Institution
  • Website: https:// www. hds.utc.fr
  • Country: France
  • City: COMPIEGNE
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