Papers Drivable Area Detection
“Drivable Area Detection” 태그가 달린 논문 12편 · 필터 해제
TriLiteNet: Lightweight Model for Multi-Task Visual Perception
Efficient perception models are essential for Advanced Driver Assistance Systems (ADAS), as these applications require rapid processing and response to ensure safety and effectiveness in real-world environments. To addre…
Autonomous DrivingComputational EfficiencyDrivable Area DetectionLane Detection+4Task-Oriented Pre-Training for Drivable Area Detection
Pre-training techniques play a crucial role in deep learning, enhancing models' performance across a variety of tasks. By initially training on large datasets and subsequently fine-tuning on task-specific data, pre-train…
Drivable Area DetectionTwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
Semantic segmentation is crucial for autonomous driving, particularly for Drivable Area and Lane Segmentation, ensuring safety and navigation. To address the high computational costs of current state-of-the-art (SOTA) mo…
Autonomous DrivingDrivable Area DetectionLane DetectionSegmentation+1TADAP: Trajectory-Aided Drivable area Auto-labeling with Pre-trained self-supervised features in winter driving conditions
Detection of the drivable area in all conditions is crucial for autonomous driving and advanced driver assistance systems. However, the amount of labeled data in adverse driving conditions is limited, especially in winte…
Autonomous DrivingDrivable Area DetectionYou Only Look at Once for Real-time and Generic Multi-Task
High precision, lightweight, and real-time responsiveness are three essential requirements for implementing autonomous driving. In this study, we incorporate A-YOLOM, an adaptive, real-time, and lightweight multi-task mo…
Autonomous DrivingDrivable Area DetectionLane Detectionobject-detection+3TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars
Semantic segmentation is a common task in autonomous driving to understand the surrounding environment. Driveable Area Segmentation and Lane Detection are particularly important for safe and efficient navigation on the r…
Autonomous DrivingAutonomous VehiclesDrivable Area DetectionGPU+4YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
Over the last decade, multi-tasking learning approaches have achieved promising results in solving panoptic driving perception problems, providing both high-precision and high-efficiency performance. It has become a popu…
Autonomous DrivingDrivable Area DetectionLane DetectionMulti-Task Learning+3HybridNets: End-to-End Perception Network
End-to-end Network has become increasingly important in multi-tasking. One prominent example of this is the growing significance of a driving perception system in autonomous driving. This paper systematically studies an …
Autonomous DrivingDrivable Area DetectionLane Detectionobject-detection+2Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images
This work tackles scene understanding for outdoor robotic navigation, solely relying on images captured by an on-board camera. Conventional visual scene understanding interprets the environment based on specific descript…
Autonomous NavigationDecision MakingDescriptiveDrivable Area Detection+5YOLOP: You Only Look Once for Panoptic Driving Perception
A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making the reasonable decision while driving. We present a panop…
Autonomous DrivingDrivable Area DetectionLane DetectionMulti-Task Learning+3Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System
Modern driver assistance systems rely on a wide range of sensors (RADAR, LIDAR, ultrasound and cameras) for scene understanding and prediction. These sensors are typically used for detecting traffic participants and scen…
3D Object DetectionAutonomous DrivingDrivable Area Detectionobject-detection+2BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to stu…
Autonomous DrivingDiversityDomain AdaptationDrivable Area Detection+8