Michele Buzzoni

VIAC: An out of ordinary experiment

This paper presents the preliminary results of VIAC, the VisLab Intercontinental Autonomous Challenge, a test of autonomous driving along an unknown route from Italy to China. It took 3 months to run the entire test; all data have been logged, including all data generated by the sensors, vehicle data, and GPS info. This huge amount of information has been packed during the trip, compressed, and transferred back to Parma for further processing. This data is now ready for a deep analysis of the various systems performance, with the aim of virtually running the whole trip multiple times with improved versions of the software. This paper discusses some preliminary figures obtained by the analysis of the data collected during the test. More information will be generated by a deeper analysis, which will take additional time, being the data about 40 terabyte in size.

Stereo obstacle detection in challenging environments: The VIAC experience

Obstacle detection by means of stereo-vision is a fundamental task in computer vision, which has spurred a lot of research over the years, especially in the field of vehicular robotics. The information provided by this class of algorithms is used both in driving assistance systems and in autonomous vehicles, so the quality of the results and the processing times become critical, as detection failures or delays can have serious consequences. The obstacle detection system presented in this paper has been extensively tested during VIAC, the VisLab Intercontinental Autonomous Challenge [1], [2], which has offered a unique chance to face a number of different scenarios along the roads of two continents, in a variety of conditions; data collected during the expedition has also become a reference benchmark for further algorithm improvements.

An embedded system for counting passengers in public transportation vehicles

This article describes a system for people counting conceived for public transportation vehicles. The underlying idea is to monitor the number of passengers getting in or out public transportation means like buses and metros over time hence computing reliable estimations in order to improve vehicles door control. A stereo vision system is presented, it has been developed considering its future installation over bus doors; a feature based people counting algorithm and an object tracking system are used to count people getting in or out of a specific region of interest. The system here described will be installed and tested on an Ivecobus Citelis vehicle in the framework of the Italian Industria 2015 Ecoautobus initiative.

Intelligent overhead sensor for sliding doors: a stereo based method for augmented efficiency

This paper describes a method to detect and extract pedestrians trajectories in proximity of a sliding door access in order to automatically open the doors: if a pedestrian walks towards the door, the system opens the door. On the other hand if the pedestrian trajectory is parallel to the door, the system does not open. The sensor is able to self-adjust according to changes in weather conditions and environment. The robustness of this system is provided by a new method for disparity image extraction. The rationale behind this work is that the device developed in this paper avoids unwanted openings in order to decrease needs for maintenance, and increase building efficiency in terms of temperature (i.e. heating and air conditioning). The algorithm has been tested in real conditions to measure its capabilities and estimate its performance.