Proceedings ArticleDOI
Panoramic 3D LiDAR-based Object Detection
Divya Kadam,Y. Chalapathi Rao,G. Harshitha +2 more
- pp 770-776
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TLDR
In this article , the authors proposed a self-driving car detection system, which uses a combination of artificial intelligence, algorithms, and sensors to navigate roads and make decisions without human intervention.Abstract:
Vehicle detection systems play a crucial role in preventing accidents by providing real-time information about the location and movement of vehicles on the road. Monitoring speed is also essential for safety purposes as it enables the identification of vehicles that are exceeding speed limits or driving recklessly under current road conditions. The emergence of self-driving cars, which use a combination of artificial intelligence, algorithms, and sensors to navigate roads and make decisions without human intervention, is a rapidly advancing technology. Despite its complexity, this technology offers numerous advantages such as increased safety and reduced traffic congestion. One of the most significant benefits of self-driving cars is the potential to reduce accidents caused by human error, which is the leading cause of traffic accidents. By eliminating the need for human drivers, the risk of accidents can be significantly reduced. The frequency of car accidents is alarming, with one occurring every minute according to the National Highway Traffic Safety Administration (NHTSA). Auto insurance industry statistics indicate that every driver is likely to encounter at least four car accidents in their lifetime. Inexperienced drivers, particularly those aged 16 to 20, are at a higher risk of being involved in accidents. Annually, approximately 37,000 people die in car accidents, with one fatal accident occurring every 16 minutes. Among these, nearly 8,000 deaths involve drivers aged between 16 and 20 years old. Shockingly, more than 1,600 children under the age of 15 lose their lives in car accidents each year. To combat this alarming trend, measures are being taken to promote safe driving and reduce the frequency of accidents. Additionally, self-driving cars have the potential to improve traffic flow and minimize travel time as the vehicles can communicate with each other to optimize traffic flow and avoid congestion.read more
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Junior: The Stanford Entry in the Urban Challenge.
Michael Montemerlo,Jan Becker,Suhrid Bhat,Hendrik Dahlkamp,Dmitri A. Dolgov,Scott M. Ettinger,Dirk Hähnel,Tim Hilden,Gabe Hoffmann,Burkhard Huhnke,Doug Johnston,Stefan Klumpp,Dirk Langer,Anthony Levandowski,Jesse Levinson,Julien Marcil,David Orenstein,Johannes Paefgen,Isaac Penny,Anna Petrovskaya,Mike Pflueger,Ganymed Stanek,David Stavens,Antone Vogt,Sebastian Thrun +24 more
TL;DR: The architecture of Junior, a robotic vehicle capable of navigating urban environments autonomously, successfully finished and won second place in the DARPA Urban Challenge, a robot competition organized by the U.S. Government.
Proceedings ArticleDOI
On the segmentation of 3D LIDAR point clouds
Bertrand Douillard,James Underwood,N. Kuntz,Vsevolod Vlaskine,A. Quadros,Peter Morton,A. Frenkel +6 more
TL;DR: This paper presents a set of segmentation methods for various types of 3D point clouds addressed using ground models of non-constant resolution either providing a continuous probabilistic surface or a terrain mesh built from the structure of a range image, both representations providing close to real-time performance.
Proceedings ArticleDOI
Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion
TL;DR: A fast algorithm able to deal with tremendous amounts of 3D Lidar measurements using a graph-based approach to segment ground and objects from 3D lidar scans using a novel unified, generic criterion based on local convexity measures is presented.
Journal IssueDOI
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Sebastian Thrun,Michael Montemerlo,Hendrik Dahlkamp,David Stavens,Andrei Aron,James Diebel,Philip Fong,John Gale,Morgan Halpenny,Gabriel M. Hoffmann,Kenny Lau,Celia M. Oakley,Mark Palatucci,Vaughan R. Pratt,Pascal Stang,Sven Strohband,Cedric Dupont,Lars-Erik Jendrossek,Christian Koelen,Charles Markey,Carlo Rummel,Joe van Niekerk,Eric Jensen,Philippe Alessandrini,Gary Bradski,Bob Davies,Scott M. Ettinger,Adrian Kaehler,Ara Nefian,Pamela Mahoney +29 more
TL;DR: The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high-speed desert driving without manual intervention using state-of-the-art artificial intelligence technologies, such as machine learning and probabilistic reasoning.
Journal ArticleDOI
3D Lidar-based static and moving obstacle detection in driving environments
TL;DR: A 3D perception system based on voxel-grid model for static and moving obstacles detection using discriminative analysis and ego-motion information and a complete framework for ground surface estimation and static/moving obstacle detection in driving environments is proposed.