Internship: Coordination and Control of Connected Autonomous Vehicles

• MERL is looking for a highly motivated individual to work on optimization-based techniques for coordination and control of connected autonomous vehicles (CAVs), in the presence of other CAVs and human driven vehicles (HDVs). The research will involve the development, implementation, and validation of optimization-based coordinated control of vehicles through traffic intersections and/or merging scenarios. The ideal candidate should have experience in either one or multiple of the following topics: vehicle modeling and/or traffic modeling, mixed-integer programming, (stochastic) model predictive control, reinforcement learning, data-driven (e.g., Gaussian Process) modeling, hybrid dynamical systems, coordination and control of CAVs. Knowledge of one or multiple vehicle and/or traffic simulators (SUMO, CARLA, CarSim, Vissim, etc.) is a plus. Publication of relevant results in conference proceedings or journals is expected. Good programming skills in Matlab are required and knowledge in Python or C/C++ is a merit. PhD students in engineering, mathematics, or similar are encouraged to apply. The expected duration of the internship is 3-6 months and the start date is flexible.

Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics

Contact: Rien Quirynen

Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

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