Obstacle detection is a fundamental task for Advanced Driver Assistance Systems (ADAS) and Self-driving cars. Several commercial systems like Adaptive Cruise Controls and Collision Warning Systems depend on them to notify the driver about a risky situation. Several approaches have been presented in the literature in the last years. However, most of them are limited to specific scenarios and restricted conditions. In this paper we propose a fast obstacle estimation to stereo cameras followed by a robust road estimation method. Our approach uses only disparity maps and works with a low number of pixels instead of the entire image. Experimental tests have been carried out in different conditions using the standard ROAD-KITTI benchmark, obtaining positive results.
A 60-year-old man was seen in the hospital with a history of conjunctival mantle cell lymphoma (MCL), which had been biopsied elsewhere. The patient had no complaints, and his examination had unremarkable results. A magnetic resonance image of the head, performed as part of a full systemic work-up, revealed lacrimal sac and nasolacrimal duct thickening and opacification on both sides. A biopsy of the right lacrimal sac disclosed lymphocytes expressing CD20, CD5, B-cell lymphoma 2, cyclin D1, and SOX-11 proteins, yielding a diagnosis of bilateral MCL of the lacrimal system (Figure). Cases of MCL arise from pregerminal naive B cells expressing aberrant cyclin D1 resulting from the translocation t[11;14](q13;q32). 1 Bilateral involvement of the lacrimal sac and nasolacrimal duct is extremely rare. 2,3 This patient had only 1 additional neoplastic site (in his bone marrow). After chemotherapy with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-maxi-CHOP) with rituximab and high-dose cytarabine (R-HIDAC), he has been free of disease for 5 years.
Road accidents is one of the major cause of deaths worldwide. Traffic is also one of the main problems in large cities. The development of intelligent transportation systems may contribute to the solution of both problems. Autonomous vehicles have been addressed by the academic community for decades and recently have been obtaining a considerable attention from the industry. One of the next steps for such technology is the development of communication systems that allow cooperative driving, which could improve the traffic efficiency. This paper proposes a simulation framework for the development of cooperative driving systems. Our framework is capable of accurate simulation of vehicles, sensors, complex environments, and wireless communication between vehicles. It is also compatible to well known robotic control middlewares, allowing portability of the develop code between real and simulated test vehicles. Results of cooperative adaptive cruise control with multiple vehicles are presented to validate the framework proposed.
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