Multimodality fusion based on deep neural networks (DNN) is a significant method for
intelligent vehicles. The special characteristics of DNN lead to the issue of AI safety and …

Object detection is one of the most important and challenging branches of computer vision,
whose main task is to classify and localize objects in images or videos. The development of …

This paper presents an intelligent autonomous document mobile delivery robot using a deep
learning approach. The robot is built as a prototype for document delivery service for use in …

The complex decision-making systems used for autonomous vehicles or advanced driver-
assistance systems (ADAS) are being replaced by end-to-end (e2e) architectures based on …

Making autonomous driving a safe, feasible, and better alternative is one of the core
problems the world is facing today. The horizon of the applications of AI and deep learning …

Autonomous Vehicles (AVs) have caught people's attention in recent years, not only from an
academic or developmental viewpoint but also because of the wide range of applications …

With the advent of deep learning, self-driving schemes based on deep learning are
becoming more and more popular. Robust perception-action models should learn from data …

Deep Learning algorithms have achieved state-of-the-art performance in various important
tasks. However, recent studies have found that an elaborate perturbation may cause a …

Various innovations on self-driving cars are trending in the automobile industry these days.
The general approach for AI applications is to collect the data through various sensors that …

We present the first prize solution to NeurIPS 2021-AWS Deepracer Challenge. In this
competition, the task was to train a reinforcement learning agent (ie an autonomous car) …