Visual Perception System for Autonomous Driving

The recent surge in interest in autonomous driving is fueled by its rapidly developing capacity to enhance safety, efficiency, and convenience. A key component of autonomous driving technology lies in its perceptual systems, where advancements have led to more precise algorithms applicable to autonomous driving, such as vision-based Simultaneous Localization and Mapping (SLAM), object detection, and tracking algorithms. This work introduces a visual-based perception system for autonomous driving that integrates trajectory tracking and prediction of moving objects to prevent collisions while addressing the localization and mapping needs of autonomous driving. The system leverages motion cues from pedestrians to monitor and forecast their movements while simultaneously mapping the environment. This integrated approach resolves camera localization and tracks other moving objects in the scene, ultimately generating a sparse map to facilitate vehicle navigation. The performance, efficiency, and resilience of this approach are demonstrated through comprehensive evaluations of both simulated and real-world datasets.

Authors

• Qi Zhang
• Siyuan Gou
• Wenbin Li

Keywords

• Location awareness
• Simultaneous localization and mapping
• Trajectory tracking
• Prediction algorithms
• Trajectory
• Safety
• Surges
• Autonomous vehicles
• Visual perception
• Resilience
• Perceptual System
• Object Detection
• Pedestrian
• Simulated Datasets
• Real-world Datasets
• Objects In The Scene
• Trajectory Prediction
• Recent Surge Of Interest
• Time Interval
• Root Mean Square Error
• Unit Vector
• Long Short-term Memory
• Simulation Environment
• Bounding Box
• Kalman Filter
• Pose Estimation
• Object Tracking
• Current Frame
• Object Trajectory
• KITTI Dataset
• Head Direction
• Prediction Vector
• Camera Pose
• Displacement Error
• Trajectory Estimation
• 3D Bounding Box
• Object In Frame
• Odometry