scholarly journals Obstacle Detection and Track Detection in Autonomous Cars

2020 ◽  
Author(s):  
Ayesha Iqbal
Keyword(s):  
Obstacle Detection ◽  
Autonomous Cars

scholarly journals Opportunities and Challenges of Machine Vision for Navigation and Obstacle Detection in Autonomous Ground Vehicles

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1598-1601
Keyword(s):  
Machine Vision ◽  
Object Detection ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Conventional Vehicle ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Autonomous Cars

Autonomous vehicles are the future of transport and also it is expected to become a fully-fledged reality within a decade. All the major giants in the automotive industry are hard pressing their transition from conventional vehicle to autonomous vehicles. The state of Karnataka, for instance, had approximately 205,200 registered taxis higher than Madhya Pradesh 174,900 registered cabs from 2014 to 2015. This presents a great deal of opportunities for autonomous cars and need for technologies. Autonomous cars reduces the accidents rate, stress free parking, saves time, reduces traffic congestion, improve fuel economy etc. It is so sophisticated to the level of easy prediction of physical objects, behavioural elements such as driving speed limits and driving rules between the physical world and its map. Autonomous vehicle have grown to an extent of updating its own information and also based on the cloud, benefitting the systems of all other cars on the network. Machine vision is the most crucial aspect which gives the autonomous vehicles the knowledge of its surrounding. This paper deals with the different approaches of machine vision that helps the vehicle in lane and obstacle detections. Few methods of obstacle detection like Single Object Detection and tracking (SODT) and Multiple Object Detection and tracking (MODT) are compared and contrasted in this paper. Despite the enormous advantages, there are still some challenges of autonomous which needs to be addressed. The challenges that the field will face, especially in relevance with India, along with the suggestion for improvement is also discussed.

scholarly journals Infrastructure-free NLoS Obstacle Detection for Autonomous Cars

2019 ◽  
Author(s):  
Felix Naser ◽  
Igor Gilitschenski ◽  
Alexander Amini ◽  
Christina Liao ◽  
Guy Rosman ◽  
...  
Keyword(s):  
Obstacle Detection ◽  
Autonomous Cars

Subtraction-Based General Forward Obstacle Detection from In-Vehicle Camera Images using Extended Census Transform

2016 ◽  
Vol 136 (4) ◽  
pp. 588-589
Author(s):  
Haruya Kyutoku ◽  
Daisuke Deguchi ◽  
Tomokazu Takahashi ◽  
Yoshito Mekada ◽  
Ichiro Ide ◽  
...  
Keyword(s):  
Obstacle Detection ◽  
Census Transform

Integration of advanced stereo obstacle detection with perspectively correct surround views

2019 ◽  
Vol 2019 (15) ◽  
pp. 32-1-32-10
Author(s):  
Christian Fuchs ◽  
Dietrich Paulus
Keyword(s):  
Obstacle Detection

Formal Verification of Control System Software

2019 ◽  
Author(s):  
Pierre-Loïc Garoche
Keyword(s):  
Control System ◽  
Formal Verification ◽  
Formal Analysis ◽  
Critical Systems ◽  
System Software ◽  
Unified Approach ◽  
Verification Methods ◽  
Autonomous Cars ◽  
Computer Scientists ◽  
Verification Techniques

The verification of control system software is critical to a host of technologies and industries, from aeronautics and medical technology to the cars we drive. The failure of controller software can cost people their lives. This book provides control engineers and computer scientists with an introduction to the formal techniques for analyzing and verifying this important class of software. Too often, control engineers are unaware of the issues surrounding the verification of software, while computer scientists tend to be unfamiliar with the specificities of controller software. The book provides a unified approach that is geared to graduate students in both fields, covering formal verification methods as well as the design and verification of controllers. It presents a wealth of new verification techniques for performing exhaustive analysis of controller software. These include new means to compute nonlinear invariants, the use of convex optimization tools, and methods for dealing with numerical imprecisions such as floating point computations occurring in the analyzed software. As the autonomy of critical systems continues to increase—as evidenced by autonomous cars, drones, and satellites and landers—the numerical functions in these systems are growing ever more advanced. The techniques presented here are essential to support the formal analysis of the controller software being used in these new and emerging technologies.

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