Autonomous Systems & Applied AI

The field of autonomous intelligent systems is at the forefront of many approaches to solving the grand challenges that face engineers worldwide.

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About This Lab

To equip engineering graduates with the skills and expertise needed to tackle these challenges, the Quanser Autonomous Systems and AI Lab Collection is a turnkey collection of platforms, libraries, course resources, and laboratory infrastructure that brings complex theories to life. To catalyze innovative research, the lab offers powerful open-architecture platforms and digital twins that accelerate development and offer a reliable and flexible design and development backbone to generations of researchers.

Self-Driving

Quanser Approach
Curriculum Learning Objectives
Self-driving Research and Education Centers

Quanser’s self-driving solution takes a turnkey, cohesive and application-centric approach to enabling education and research in this space. Built on an open-architecture foundation and designed to be language agnostic, this unified ecosystem covers ground in both state-of-the-art hardware as well as support for software APIs and frameworks already in use by academia and industry alike.

Access to both a fleet of NVIDIA powered 1/10th scale research vehicles, infrastructure such as smart traffic lights, and a self-driving cityscape map creates a autonomy sandbox for self-driving courses and research endeavors. Further access to a digital twin and equivalent of the hardware and environment via Quanser
Interactive Labs further enables academics looking to verify code via a dynamic simulation, or generate datasets with complex environments otherwise not feasible in a lab setting, such as 50km of highway.

Finally, advanced curriculum targeting relevant self-driving concepts such as lane-keeping, state estimation, lateral Stanley control, behavior planning, etc., provide students with safe hands-on activities to explore autonomy in the field through cascaded lab experiences.

Develop foundational self-driving engineering literacy
Gain operational skills in the practical software implementation of self-driving algorithms
Explore and compare the performance and accuracy of analytical vs. machine learning
approaches to perception
Explore visual, ranging and odometric sensing capabilities and multi-sensor & pr obabilistic
fusion technologie
Explore multi-agent complex driving scenarios and ego-vehicle decision making pipelines