A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the performance of existing 2D detection systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3) and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context. For the experimental study, we use the Waymo Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage detectors, our findings show that two-stage detectors still provide the most robust performance. Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage.

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Deep learning based Blockchain solution for preserving privacy in future vehicles

International Journal of Hybrid Intelligent Systems ◽

10.3233/his-200289 ◽

2020 ◽

pp. 1-14

Author(s):

Varsha R ◽

Meghna Manoj Nair ◽

Siddharth M. Nair ◽

Amit Kumar Tyagi

Keyword(s):

Deep Learning ◽

Autonomous Vehicles ◽

Recommendation System ◽

Privacy Preserving ◽

Smart Devices ◽

The Road ◽

Blockchain Technology ◽

Technological Advances ◽

Previous Decade ◽

Smart Things

The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today’s generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system.

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An Enhanced Multi-Stage Deep Learning Framework for Detecting Malicious Activities From Autonomous Vehicles

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2021.3105834 ◽

2021 ◽

pp. 1-10

Author(s):

Izhar Ahmed Khan ◽

Nour Moustafa ◽

Dechang Pi ◽

Waqas Haider ◽

Bentian Li ◽

...

Keyword(s):

Deep Learning ◽

Autonomous Vehicles ◽

Learning Framework ◽

Multi Stage

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Scene Understanding in Deep Learning-Based End-to-End Controllers for Autonomous Vehicles

IEEE Transactions on Systems Man and Cybernetics Systems ◽

10.1109/tsmc.2018.2868372 ◽

2019 ◽

Vol 49 (1) ◽

pp. 53-63 ◽

Cited By ~ 7

Author(s):

Shun Yang ◽

Wenshuo Wang ◽

Chang Liu ◽

Weiwen Deng

Keyword(s):

Deep Learning ◽

Autonomous Vehicles ◽

Scene Understanding ◽

End To End

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Imitating a Safe Human Driver Behaviour in Roundabouts Through Deep Learning

Science & Technique ◽

10.21122/2227-1031-2020-19-1-85-88 ◽

2020 ◽

Vol 19 (1) ◽

pp. 85-88

Author(s):

A. S. J. Cervera ◽

F. J. Alonso ◽

F. S. García ◽

A. D. Alvarez

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Driver Behaviour ◽

Safety Issues ◽

Current State ◽

Surrounding Environment ◽

Human Driver ◽

State Of The Environment

Roundabouts provide safe and fast circulation as well as many environmental advantages, but drivers adopting unsafe behaviours while circulating through them may cause safety issues, provoking accidents. In this paper we propose a way of training an autonomous vehicle in order to behave in a human and safe way when entering a roundabout. By placing a number of cameras in our vehicle and processing their video feeds through a series of algorithms, including Machine Learning, we can build a representation of the state of the surrounding environment. Then, we use another set of Deep Learning algorithms to analyze the data and determine the safest way of circulating through a roundabout given the current state of the environment, including nearby vehicles with their estimated positions, speeds and accelerations. By watching multiple attempts of a human entering a roundabout with both safe and unsafe behaviours, our second set of algorithms can learn to mimic the human’s good attempts and act in the same way as him, which is key to a safe implementation of autonomous vehicles. This work details the series of steps that we took, from building the representation of our environment to acting according to it in order to attain safe entry into single lane roundabouts.

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