Pier Paolo Porta

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.

The Single Frame Stereo Vision System for Reliable Obstacle Detection Used during the 2005 DARPA Grand Challenge on TerraMax

Autonomous driving in off-road environments requires an exceptionally capable sensor system, especially given that the unstructured environment does not provide many of the cues available in on-road environments. This paper presents a variable-width-baseline (up to 1.5 meters) single-frame stereo vision system for obstacle detection that can meet the needs of autonomous navigation in extreme environments. Efforts to maximize computational speed oth in the attention given to accurate and stable calibration and the exploitation of the processors MMX and SSE instruction sets - allow a guaranteed 15 fps rate. Along with the assured speed, the system proves very robust against false positives. The system has been field tested on the TerraMax vehicle, one of only five vehicles to complete the 2005 DARPA Grand Challenge course and the only one to do so using a vision system for obstacle detection

TerraMax Vision at the Urban Challenge 2007

This paper presents the TerraMax vision systems used during the 2007 DARPA Urban Challenge. First, a description of the different vision systems is provided, focusing on their hardware configuration, calibration method, and tasks. Then, each component is described in detail, focusing on the algorithms and sensor fusion opportunities: obstacle detection, road marking detection, and vehicle detection. The conclusions summarize the lesson learned from the developing of the passive sensing suite and its successful fielding in the Urban Challenge.

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.

The VisLab Intercontinental Autonomous Challenge: An Extensive Test for a Platoon of Intelligent Vehicles

This paper presents the VisLab Intercontinental Autonomous Challenge (VIAC), an autonomous vehicles test carried out from Parma to Shanghai between July and October 2010 by the VisLab team. The vehicle equipment is explained introducing the sensing systems which were tested during the journey. Trip details and the first statistics are presented as well.

The passive sensing suite of the TerraMax autonomous vehicle

This paper presents the TerraMax autonomous vehicle, which competed in the DARPA Urban Challenge 2007. The sensing system is mainly based on passive sensors, in particular four vision subsystems are used to cover a 360deg area around the vehicle, and to cope with the problems related to complex traffic scenes navigation. A trinocular system derived from the one used during the 2005 Grand Challenge performs obstacle and lane detection, twin stereo systems (one in the front and one in the back) monitor the area close to the truck, two lateral cameras detect oncoming vehicles at intersections, and a rear view system monitors the lanes next to the truck looking for overtaking vehicles. Data fusion between laser scanners and vision will be discussed, focusing on the benefits of this approach.

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%.

An algorithm for Italian de-restriction signs detection

Color has proved to be an important feature to be exploited for road signs detection on images; however, not all road signs have distinctive color characteristics. This paper presents a shape-based approach for Italian de-restriction signs detection; the developed algorithm uses a black band extractor to highlight regions of interest, where a circle shape detection is performed. Tracking is used in order to increase reliability. The obtained detector is robust to different illumination conditions and shadows, and can manage different kinds of noise and perturbation. Despite its sensitiveness, the detector showed few false positives during performed tests.

A Laserscanner-Vision Fusion System Implemented on the TerraMax Autonomous Vehicle

This paper presents a sensor fusion model developed for the 2005 Grand Challenge competition, an autonomous ground vehicle race across the Mojave desert organized by DARPA. The two sensors used in this work are a stereo vision camera pair and an ALASCA laserscanner. An algorithm to filter laserscanners raw scan data and to remove ground reflections is also presented. Several tests were made to prove the reliability of this method, that has proved to be useful to extract the information required by the race. Fusion was performed both at a low and medium level: terrain slope, detected with stereo vision, was used to correct pitch information of laserscanner raw data. Object segmentation is applied on a bird view bitmap where each pixel represents a square area of the world in front of the vehicle; this bitmap is obtained from the fusion of the ones generated by each sensor

GPU implementation of a road sign detector based on particle swarm optimization

Road Sign Detection is a major goal of the Advanced Driving Assistance Systems. Most published work on this problem share the same approach by which signs are first detected and then classified in video sequences, even if different techniques are used. While detection is usually performed using classical computer vision techniques based on color and/or shape matching, most often classification is performed by neural networks. In this work we present a novel modular and scalable approach to road sign detection based on Particle Swarm Optimization, which takes into account both shape and color to detect signs. In our approach, in particular, the optimization of a single fitness function allows both to detect a sign belonging to a certain category and, at the same time, to estimate its position with respect to the camera reference frame. To speed up processing, the algorithm implementation exploits the parallel computing capabilities offered by modern graphics cards and, in particular, by the Compute Unified Device Architecture by nVIDIA. The effectiveness of the approach has been assessed on both synthetic and real video sequences, which have been successfully processed at, or close to, full frame rate.