Connected and Autonomous Vehicles (CAVs) take advantage of the advancement of

communication and sensing technologies to offer a potential sustainable alternative to current

mobility services. Many ongoing projects are studying the effects of CAVs on the network, at

the same time trying to identify the best strategies to develop in order to design new and

dedicated traffic control strategies. The challenge here is to solve the medium-term situation

where both conventional and automated traffic will share the road network.

Very first real-world deployments have been carried out experimentally and tend to confirm

the expected benefits of cooperation between AVs (Stern et al., 2018), that have been

previously identified in simulation (Guériau et al., 2016). However, the high cost of running

such full scale tests (with relatively small amount of vehicles) is one of the reason CAV

technologies deployment takes more time than expected. Indeed, autonomous driving

algorithms, especially when built using Artificial Intelligence techniques and learning-based

approaches, require substantial quantities of data and experience that cannot be solely

provided by actual field-tests.

The deep-learning community is being more and more aware that machine learning models

are “costly to train and develop, both financially, due to the cost of hardware and electricity or

cloud compute time, and environmentally, due to the carbon footprint required to fuel modern

tensorprocessing hardware”1. This is led to the emergence of an interesting and novel research

effort, called Green AI in the litterature (Schwartz et al.,2019), that aims at reducing the amount

of data required for deep-learning based approaches to converge. One the the strategies

“which has been recently gaining importance to drastically reduce computational time and

energy consumed is to exploit the availability of different information sources”1, or

different models, environments.

Simulation and robotic models (such as in Hyldmar et al., 2019) appear to be the fastest

way to first develop, train and then test autonomous driving tasks, allowing the system

designer to investigate the behaviour of embedded navigation systems in a wide range of

situations and/or conditions and across different environments (including for instance

adverse weather conditions that affect vision-based perception, Blin et al. 2020). This process

however faces several challenges: reality gap, when a simulation/model fails to capture all

the particularities of a real system, and domain adaptation, when a model is

developped/trained in a particular context and has to adapt to a different one.

In this context, techniques akin to transfer learning (TL) have been developed to enable

knowledge acquired by one or multiple (Taylor et al., 2019) learner (source) agents to be

transferred to another or the same (target) system, helping the latter to learn a similar but

different task or to adapt an existing algorithm to a similar domain. Recently, TL was shown to

be particularly efficient when transferring autonomous driving tasks, between different

domains (Sharma et al., 2019) and from simulation to a real system (Balaji et al., 2019).


The work intended within this Post-doc position is part of a research project named

MultiTrans, that focuses on exploring novel TL approaches for autonomous vehicles scene

semantic segmentation and detection accross 3 different environments (as depicted in the

Figure above: simulation, a robotic platform and a real-world autonomous shuttle test-bed).

This position is funded by the Agence Nationale de la Recherche (ANR) under grant reference




The main objective is to build a novel autonomous vehicle virtualization framework

(digital twin) enabling to investigate and propose new algorithms that rely on transfer

learning techniques.

More specifically, the work is expected to contribute to the following objectives within

MultiTrans project:

-       Identification of critical applications requiring multi-domain transfer (extracted from

real-world domains);

-       Scripting of base use cases, small variations (taking advantage of computer power in

simulation), major variations (that require realistic alterations of sensing capabilities)

and corner cases (that could be modelled and tested in the robotic testbed);

-       Development of a virtualization framework allowing simulation environments and real

environments to benefit from each other.

Expected contributions and research outreach

The work undertaken by the successful candidate should contribute and is not limited to:

-       a better understanding of issues related to implementing autonomous driving across different domains: reality gap, overfitting, few-shots learning, experimental biases, etc;

-        insights on the use of simulation and robotics environments to foster and accelerate the development and deployment of connected and autonomous driving technologies.

-       novel approaches to transfering and acquiring knowledge from simulation to realworld

and from real-world to simulation;

Given that part of the research will be led jointly using a robotic environment and the newly

developped digital twin, it is expected that the framework developped will allow for reproducible experiments that could be used as demonstrators for research/teaching but also for disseminating material (such as video recordings) to the public.


 R.E. Stern, S. Cui, M.L. Delle Monache, R. Bhadani, M. Bunting, M. Churchill, N. Hamilton, H.

Pohlmann, F. Wu, B. Piccoli and B. Seibold, “Dissipation of stop-and-go waves via control of

autonomous vehicles: Field experiments”, Transportation Research Part C: Emerging

Technologies, 2018, 89, pp.205-221.

 M. Guériau, R. Billot, N.E. El Faouzi, J. Monteil, F. Armetta, S. Hassas, “How to assess the benefits

of connected vehicles? A simulation framework for the design of cooperative traffic

management strategies”, Transportation research part C: emerging technologies, 2016, 67, pp.


 R. Schwartz, J. Dodge, N. A. Smith, O. Etzioni. “Green ai.” arXiv preprint arXiv:1907.10597, 2019.

 N. Hyldmar, Y. He and A. Prorok, "A Fleet of Miniature Cars for Experiments in Cooperative

Driving," 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 3238-


 R. Blin, S. Ainouz, S. Canu and F. Meriaudeau, “A New Multimodal RGB and Polarimetric Image

Dataset for Road Scenes Analysis”, IEEE/CVF Conference on Computer Vision and Pattern

Recognition Workshops, 2020, pp. 216-217.

 A. Taylor, I. Dusparic, M. Guériau, S. Clarke, “Parallel transfer learning in multi-agent systems:

What, when and how to transfer?”, International Joint Conference on Neural Networks (IJCNN),

2019, pp. 1-8.

 S. Sharma, J.E. Ball, B. Tang, D.W. Carruth, M. Doude, M.A. Islam, ”Semantic segmentation with

transfer learning for off-road autonomous driving”, Sensors, 2019, 19(11), pp. 2577.

 B. Balaji, S. Mallya, S. Genc, S. Gupta, L. Dirac, V. Khare, G. Roy et al., “Deepracer: Autonomous

racing platform for experimentation with sim2real reinforcement learning”, IEEE International

Conference on Robotics and Automation (ICRA), 2020, pp. 2746-2754.

How to apply ?


Simulation, robotics, transfer learning, autonomous driving, computer vision, deep

(reinforcement) learning.

Qualification and skills

The successful candidate would:

-        have completed a PhD. in Computer Science or Robotics with a specialization/interest

in Robotics, Simulation, AI- and/or machine learning-based techniques;

-       have demonstrated research experience and relevant publication records;

-        have strong English and/or French writing and oral communication skills.

Knowledge and/or experience with the following fields would be greatly appreciated:

-        robotics environments (ROS, etc.) and/or vehicular simulation (CARLA, SUMO,etc.);

-       intelligent transportation systems, connected and automated vehicles;

-       AI techniques such as deep learning, reinforcement learning, transfer learning, multiagent systems.


Maxime Guériau (Assistant Professor) and Samia Ainouz (Professor), both member of the

Intelligent Transportation Systems team (STI) at LITIS lab (Laboratoire d'Informatique, de

Traitement de l'Information et des Systèmes), INSA Rouen Normandy, France.

About LITIS lab and the STI team

The research conducted at LITIS lab covers 3 major fields: information access, bio-medical

information processing and ambient intelligence with applications in health, automotive and

smart territories. The expertise of LITIS members is recognized internationally and includes:

machine learning, multi-agent systems, intelligent vehicles. The STI team (the successful

candidate will be joining) is specialized in advanded driving assistance systems, computer

vision, distributed and autonomous systems.

The LITIS is a laboratory (EA 4108) of University of Rouen Normandy, University of Havre

Normandy and INSA Rouen Normandy. It is a member of the doctoral school MIIS and of the

normand network «Digital Normandy». LITIS is a partner of the Normastic CNRS Research


Address: 685 Avenue de l'Université, 76800 Saint-Étienne-du-Rouvray.

The candidate will be allowed to access to different experimental platforms to carry out the


-       a robotic platform featuring different robot cars equipped with state-of-the-art perception sensors;

-       an autonomous shuttle test-bed with equipped infrastructure;

-       an intensive computing center (CRIANN: Centre Régional Informatique et d'Applications Numériques de Normandie).

Salary, starting date, travel and support

This 2-year (24-month) Post-doc position is funded by the Agence Nationale de la Recherche

(ANR) under grant reference ANR-21-CE23-0032.

Salary: 2300€ net/month

Expected starting date: around Sept. 2022.

The successful candidate will receive support for occasional international travel (participation

to conferences). Occasional national travel (mainly to Nice and Paris) will be organised,

enabling the candidate to visit the partners of MultiTrans project.

Dissemination and visibility of the work will be ensured by advertising it on MultiTrans

website and sharing resources, open-source algorithms, framework and findings (on a Git


Application process

Candidates applications should include:

-       a full resume, including a comprehensive list of publications, and;

-       a cover letter, and;

-       contact details of up to 2 references;

And be sent to:, ,

By no later than May 27 th 2022 .