In this review, we provide an overview of emerging trends and challenges in the field of
intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of …

Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

In this paper, we propose a survey of the Simultaneous Localization And Map** (SLAM)
field when considering the recent evolution of autonomous driving. The growing interest …

Visual understanding of complex urban street scenes is an enabling factor for a wide range
of applications. Object detection has benefited enormously from large-scale datasets …

Autonomous vehicles are expected to play a key role in the future of urban transportation
systems, as they offer potential for additional safety, increased productivity, greater …

Intelligent vehicles have increased their capabilities for highly and, even fully, automated
driving under controlled environments. Scene information is received using onboard …

Semantic understanding of urban street scenes through visual perception has been widely
studied due to many possible practical applications. Key challenges arise from the high …

This paper considers an architecture referred to as Cascade Region Proposal Network
(Cascade RPN) for improving the region-proposal quality and detection performance by …

This paper focuses on the real-time trajectory planning problem for autonomous vehicles
driving in realistic urban environments. To solve the complex navigation problem, we adopt …

Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and
small, provide appearance information about the environment, and work in various weather …