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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Evaluating Autonomous Urban Perception and Planning in a 1/10th Scale MiniCity.

Noam Buckman¹, Alex Hansen¹, Sertac Karaman²

¹Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Sensors (Basel, Switzerland)

|September 23, 2022

Summary

MiniCity is a scaled urban environment for testing autonomous vehicle perception systems. This platform evaluates sensor and algorithm performance in realistic driving scenarios, improving safety and reliability.

Keywords:

autonomous vehicles mobile perception multi-robot systems robot platforms

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Area of Science:

Robotics and Artificial Intelligence
Computer Vision for Autonomous Systems

Background:

Autonomous vehicles require robust perception systems for safe navigation.
Evaluating perception hardware and software in realistic urban environments is challenging.

Purpose of the Study:

To introduce MiniCity, a novel 1/10th scale urban evaluation platform for autonomous vehicle perception.
To demonstrate MiniCity's utility in assessing perception algorithms and sensor configurations.

Main Methods:

Developed a 1/10th scale realistic urban environment with intersections and autonomous vehicles.
Integrated state-of-the-art sensors and algorithms onto the scaled vehicles.
Designed urban driving scenarios, including occluded intersections and multi-vehicle interactions.

Main Results:

Successfully evaluated perception algorithm performance for object detection and localization.
Assessed the impact of different sensor and algorithm configurations on perception accuracy.
Demonstrated MiniCity's capability to evaluate downstream tasks like collision avoidance and lane following.

Conclusions:

MiniCity provides a versatile and scalable platform for rigorous testing of autonomous vehicle perception systems.
The platform facilitates the evaluation of both perception tasks and their influence on vehicle control.
MiniCity enables systematic assessment of sensor and algorithm trade-offs in complex urban driving scenarios.