Curb Detection in Urban Traffic Scenarios Using LiDARs Point Cloud and Semantically Segmented Color Images

S.E.C. Deac, I. Giosan, S. Nedevschi Proceeding of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zeeland, 26-30 October,2019, pp. 3433-3440. In this paper we propose a robust curb detection method which is based on the fusion between semantically labeled camera images and a 3D point cloud coming from LiDAR sensors. The labels from the semantically enhanced cloud are used to reduce the curbs’ searching area. Several spatial cues are next computed on each candidate curb region. Based on these features, a candidate curb region is either rejected or refined for obtaining a precise positioning of the curb points...

Real-Time Semantic Segmentation-Based Stereo Reconstruction

V.C. Miclea, S. Nedevschi IEEE Transactions on Intelligent Transportation Systems (Early Access), pp. 1-11, 2019, DOI: 10.1109/TITS.2019.2913883. In this paper, we propose a novel semantic segmentation-based stereo reconstruction method that can keep up with the accuracy of the state-of-the art approaches while running in real time. The solution follows the classic stereo pipeline, each step in the stereo workflow being enhanced by additional information from semantic segmentation. Therefore, we introduce several improvements to computation, aggregation, and optimization by adapting existing techniques to integrate additional surface information given by each semantic class. For the cost computation and optimization steps, we propose...

Efficient instance and semantic segmentation for automated driving

A. Petrovai, S. Nedevschi Proceeding of 2019 IEEE Intelligent Vehicles Symposium (IV 2019), Paris, France, 9 – 12 June, 2019, pp. 2575-2581. Environment perception for automated vehicles is achieved by fusing the outputs of different sensors such as cameras, LIDARs and RADARs. Images provide a semantic understanding of the environment at object level using instance segmentation, but also at background level using semantic segmentation. We propose a fully convolutional residual network based on Mask R-CNN to achieve both semantic and instance level recognition. We aim at developing an efficient network that could run in real-time for automated driving applications without...

Environment Perception Architecture using Images and 3D Data

H. Florea, R. Varga, S. Nedevschi Proceedings of 2018 14th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2018, pp. 223-228. This paper discusses the architecture of an environment perception system for autonomous vehicles. The modules of the system are described briefly and we focus on important changes in the architecture that enable: decoupling of data acquisition from data processing; synchronous data processing; parallel computation on GPU and multiple CPU cores; efficient data passing using pointers; adaptive architecture capable of working with different number of sensors. The experimental results compare execution times before and...

A Fast RANSAC Based Approach for Computing the Orientation of Obstacles in Traffic Scenes

F. Oniga, S. Nedevschi Proceedings of 2018 14th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2018, pp. 209-214. A low complexity approach for computing the orientation of 3D obstacles, detected from lidar data, is proposed in this paper. The proposed method takes as input obstacles represented as cuboids without orientation (aligned with the reference frame). Each cuboid contains a cluster of obstacle locations (discrete grid cells). First, for each obstacle, the boundaries that are visible for the perception system are selected. A model consisting of two perpendicular lines is fitted to the set...

Deliverable 4.4

Final specification and design of on-board sensing This deliverable states the updates on two major aspects since Deliverable 4.1: The first aspect consists in the detailed specification of the perception goals and of the sensor model of the environment. The second aspect consists in the selection and the definition of the robust and redundant perception solution for each individual perception task based on the available or new sensors. pdf

Deliverable 4.3

Initial version of higher-level perception functions The deliverable provides an initial design and implementation of the higher level perception functions referring to road surface and obstacle perception, parking spot detection, road users classification, tracking and signaling perception. pdf

Deliverable 4.2

Initial version of low-level perception functions The deliverable provides an initial design and implementation of the spatio-temporal and appearance based low level representation (STAR) which represents the basis of building a virtual super-sensor that may perceive the environment like it has the capabilities of all available sensors mounted on the vehicles. pdf

Deliverable 4.1

Initial specification and design of on-board sensing This deliverable states the sensing possibilities, suitable to enable vehicle’s highly automated driving capabilities, as well as to collect useful information for map related operations including map enrichment, alignment, etc. pdf

Fusion Scheme for Semantic and Instance-level Segmentation

Arthur Daniel Costea, Andra Petrovai, Sergiu Nedevschi Proceedings of 2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC 2018), Maui, Hawaii, USA, 4-7 Nov. 2018, pp. 3469-3475 A powerful scene understanding can be achieved by combining the tasks of semantic segmentation and instance level recognition. Considering that these tasks are complementary, we propose a multi-objective fusion scheme which leverages the capabilities of each task: pixel level semantic segmentation performs well in background classification and delimiting foreground objects from background, while instance level segmentation excels in recognizing and classifying objects as a whole. We use a fully convolutional residual network...