Paolo Medici

Real Time Road Signs Recognition

This paper presents a road signs detection and classification system based on a three-step algorithm composed of color segmentation, shape recognition, and a neural network. The final goal of this algorithm is to detect and classify almost all road signs present along Italian roads. Color segmentation was suggested by the aim to achieve real time execution, since color-based segmentation is faster than the one based on shape. In order to save computational time, only the RGB color space, directly supplied by the chosen camera, or color spaces that can be obtained with linear transformations, are considered. Two different methods are used for shape detection, one is based on pattern matching with simple models and the other one is based on edge detection and geometrical cues. The complete set of signs taken in account has been divided in several categories according to their shape and color. Finally for each road signs set a neural network is built and trained.

Autonomous vehicles control in the VisLab Intercontinental Autonomous Challenge

Autonomous driving is one of the most interesting fields of research, with a number of important applications, like agricultural, military and, most significantly, safety. This paper addresses the problem of designing a general purpose path planner and its associated low level control for autonomous vehicles operating in unknown environments. Different kinds of inputs, like the results of obstacle detection, ditch localization, lane detection, and global path planning information are merged together using potential fields to build a representation of the environment in real-time; kinematically feasible trajectories, based on vehicle dynamics, are generated on a cost map. This approach demonstrated both flexibility and reliability for vehicle driving in very different environments, including extreme road conditions. This controller was extensively tested during VIAC, the VisLab Intercontinental Autonomous Challenge, a 13,000 km long test for intelligent vehicle applications. The results, collected during the development stage and the experiment itself, are presented in the final part of this article.

Obstacle detection and classification fusing radar and vision

This paper presents a system whose aim is to detect and classify road obstacles, like pedestrians and vehicles, by fusing data coming from different sensors: a camera, a radar, and an inertial sensor. The camera is mainly used to refine the vehiclespsila boundaries detected by the radar and to discard those who might be false positives; at the same time, a symmetry based pedestrian detection algorithm is executed, and its results are merged with a set of regions of interest, provided by a Motion Stereo technique.

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.

PROUD-Public road urban driverless test: Architecture and results

The presence of autonomous vehicles on public roads is becoming a reality. In the last 10 years, autonomous prototypes have been confined in controlled or isolated environments, but new traffic regulations for testing and direct automotive companies interests are moving autonomous vehicles tests on real roads. This paper presents a test on public urban roads and freeways that was held in Parma on July 12, 2013. This was the first test in open public urban roads with nobody behind the steering wheel: the vehicle had to cope with roundabouts, junctions, pedestrian crossings, freeway junctions, traffic lights, and regular traffic. The vehicle setup, the software architecture, and the route are here presented together with some results and possible future improvements.

GOLD: A Framework for Developing Intelligent-Vehicle Vision Applications

To develop real-time vision applications for use in highly dynamic environments, such as automotive traffic, researchers must gather large amounts of data from different sensors and systems at different rates. Software capable of real-time data acquisition, synchronization, logging, and - ultimately - data processing and visualization is fundamentally important to improving researcher efficiency. The general obstacle and lane detection framework supports different devices and makes it easy to add new system functionalities. GOLD can easily become the engine for many automotive applications, and it could work in other application domains as well.

Robust real-time traffic light detection and distance estimation using a single camera

A method to detect traffic lights both during day and night is designed.A mixed method based on fuzzy logic and sequential rules is developed.The distance between vehicle and traffic light is calculated using Bayesian filters.Different results and tests are presented to validate the method. This paper presents a robust technique to detect traffic lights during both day and night conditions and estimate their distance. The traffic light detection is based initially on color properties. To enhance the color on the video sequences, the acquisition is adapted according to the luminosity of the pixels on the top of the image. A fuzzy clustering provides a better division of the traffic light colors. The traffic light color properties have been estimated from registered sequences including both colors from LED spot lights and from traditional light bulbs. The filters rules based on the traffic light aspect ratios as well as the tracking stage are used to decide whether the spots on the frames are likely to be traffic lights. Then, the distance between traffic lights and the autonomous vehicle is estimated by applying Bayesian filters to the traffic lights represented on the frames. The tests are validated with more than an hour in real urban scenarios during day and night. The paper shows that the developed advanced driver assistance system is able to detect the traffic lights with 99.4% of accuracy in the range of 10-115m. The utility of this system has been demonstrated during the Public ROad Urban Driverless car test in Italy in 2013.

Development of the control system for the Vislab Intercontinental Autonomous Challenge

This paper presents the control system of an autonomous vehicle capable of perceiving and describing the environment using different inputs, such as GPS waypoints, roadways borders and lines, leader vehicles, and obstacles to be avoided. The controller has been implemented and tested for the VisLab Intercontinental Autonomous Challenge, a long intercontinental trip that aims to demonstrate capabilities of modern autonomous vehicles. To fulfill this mission a general-purpose real-time motion planning system was designed and implemented. This pathplanner, based on the estimation of feasible trajectories on a cost map, is described and analyzed. System performance has been evaluated during tests: experimental results have demonstrated the capability of the system in vehicle following.

Real time road signs classification

This paper describes a method for classifying road signs based on a single color camera mounted on a moving vehicle. The main focus will be on the final neural network based classification stage of the candidates provided by an existing traffic sign detection algorithm. Great attention is paid to image preprocessing in order to provide a more simple and clear input to the network: candidate color images are cropped and converted to greyscale, then enhanced using a contrast stretching technique; a multi-layer perceptron neural network is then used to provide a matching score with different road sign models. Finally results are filtered using tracking. Benchmarks are presented, showing that the system is able to classify more then 200 different Italian road sign in real-time, with a recognition rate of 80% to 90%.

PROUD—Public Road Urban Driverless-Car Test

This paper presents an autonomous driving test held in Parma on urban roads and freeways open to regular traffic. During this test, the vehicle not only performed simple maneuvers, but it had to cope with complex driving scenarios as well, including roundabouts, junctions, pedestrian crossings, freeway junctions, and traffic lights. The test demonstrated the ability of the current technology to manage real situations and not only the well-structured and predictable ones. A comparison of milestones, challenges, and key results in autonomous driving is presented to highlight the novelty and the specific purpose of the test. The whole system is described: the vehicle; the software architecture; details about high-, medium-, and low-level control; and details about perception algorithms. A conclusion highlights the achieved results and draws possible directions for future development.