Fast traffic sign recognition using color segmentation and deep convolutional networks

The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelli- gent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Ori- ented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a pre- processing step to reduce the search space, while classication is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traf- c Sign data set and on the novel Data set of Italian Trac Signs (DITS), which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night-time. Experimental results demonstrate the eectiveness of the proposed ap- proach in terms of both classication accuracy and computational speed

Advanced Driver-Assistance System with Traffic Sign Recognition for Safe and Efficient Driving

Advanced Driver-Assistance Systems (ADAS) coupled with traffic sign recognition could lead to safer driving environments. This study presents a sophisticated, yet robust and accurate traffic sign detection system using computer vision and ML, for ADAS. Unavailability of large local traffic sign datasets and the unbalances of traffic sign distribution are the key bottlenecks of this research.  Hence, we choose to work with support vector machines (SVM) with a custom-built unbalance dataset, to build a lightweight model with excellent classification accuracy.  The SVM model delivered optimum performance with the radial basis kernel, C=10, and gamma=0.0001. In the proposed method, same priority was given to processing time (testing time) and accuracy, as traffic sign identification is time critical. The final accuracy obtained was 87% (with confidence interval 84%-90%) with a processing time of 0.64s (with confidence interval of 0.57s-0.67s) for correct detection at testing, which emphasizes the effectiveness of the proposed method

Electronic Interface for Lidar System and Smart Cities Applications

This work deals with the design of a new readoutelectronics for silicon photomultipliers sensors. The so-calledSiPMs sensors are an emerging technology currentlydiffusing in many applications and, among them, in thedefinition of a new generation of LIDAR systems. Thelatter, nowadays have a primary role in the evolutionprocess that is involving Smart Cities, being an enablingtechnology in different fields. The solution here proposed isrealized at electronic level with a 150 nm technology processfrom LFoundry and results provide a feasibledemonstration of the capability of the proposed designapproach to be employed in practical application

Statistical decision methods in the presence of linear nuisance parameters and despite imaging system heteroscedastic noise: Application to wheel surface inspection

International audienceThis paper proposes a novel method for fully automatic anomaly detection on objects inspected using an imaging system. In order to address the inspection of a wide range of objects and to allow the detection of any anomaly, an original adaptive linear parametric model is proposed; The great flexibility of this adaptive model offers highest accuracy for a wide range of complex surfaces while preserving detection of small defects. In addition, because the proposed original model remains linear it allows the application of the hypothesis testing theory to design a test whose statistical performances are analytically known. Another important novelty of this paper is that it takes into account the specific heteroscedastic noise of imaging systems. Indeed, in such systems, the noise level depends on the pixels’ intensity which should be carefully taken into account for providing the proposed test with statistical properties. The proposed detection method is then applied for wheels surface inspection using an imaging system. Due to the nature of the wheels, the different elements are analyzed separately. Numerical results on a large set of real images show both the accuracy of the proposed adaptive model and the sharpness of the ensuing statistical test

An experiment and simulation study on developing algorithms for CAVs to navigate through roadworks

Navigating through roadworks represents one of the main sources of safety risk for Connected and Autonomous Vehicles (CAVs) due to the altered road layouts. The built-in base maps do not normally reflect these changes, causing CAVs to experience difficulties in sensing and trajectory generation. Therefore, the objective of this paper is to evaluate different collision-free trajectory generation for CAVs at roadworks to improve safety and traffic performance. Trajectory generation algorithms using lane-level dynamic maps were examined for: 1) CAVs rely on data from in-vehicle sensor only; and 2) CAVs receive additional information via a Smart Traffic Cone (STC) in advance regarding roadwork configurations. Experiments were conducted at a controlled motorway facility operated by National Highways (England) using a vehicle instrumented with a suite of sensors. Schematics of the roadworks scenario were translated into an integrated simulation platform consisting of a traffic microsimulation (VISSIM) to simulate traffic dynamics and a sub-microscopic simulator (PreScan) capable of simulating vehicle autonomy and connectivity. Results indicate that traffic conflicts and delays decrease by 40% and 3% respectively when CAVs receive additional information in advance (i.e., Scenario 2) compared to the other scenario. These findings would assist road network operators in developing ‘CAV-enabled roadworks’ and vehicle manufacturers in designing a vehicle-based ‘roadworks assist’ system