Search Results for author:
Found 11 papers, 2 papers with code
Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review
Grid-centric perception is a crucial field for mobile robot perception and navigation.
FusionMotion: Multi-Sensor Asynchronous Fusion for Continuous Occupancy Prediction via Neural-ODE
This paper investigates multi-sensor spatio-temporal fusion strategies for continuous occupancy prediction in a systematic manner.
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions
However, training AVs usually requires a large amount of training data collected from different driving environments (e. g., cities) as well as different types of personal information (e. g., working hours and routes).
Bridging the View Disparity Between Radar and Camera Features for Multi-modal Fusion 3D Object Detection
A novel method that realizes the feature-level fusion under the bird's-eye view (BEV) for a better feature representation is proposed.
Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles
To this end, this work proposes a trajectory planner to consider the prediction model uncertainty arising from insufficient data for safer performance.
SRCN3D: Sparse R-CNN 3D Surround-View Camera Object Detection and Tracking for Autonomous Driving
Compared to prior arts, our novel sparse feature sampling module only utilizes local 2D region of interest (RoI) features calculated by projection of 3D proposal boxes for further box refinement, leading to an effective, fast and lightweight pipeline.
BE-STI: Spatial-Temporal Integrated Network for Class-Agnostic Motion Prediction With Bidirectional Enhancement
In this paper, we propose a novel Spatial-Temporal Integrated network with Bidirectional Enhancement, BE-STI, to improve the temporal motion prediction performance by spatial semantic features, which points out an efficient way to combine semantic segmentation and motion prediction.
PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving
The accelerating development of autonomous driving technology has placed greater demands on obtaining large amounts of high-quality data.
Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control
Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments.
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning
This paper proposes a driving-policy adaptive safeguard (DPAS) design, including a collision avoidance strategy and an activation function.
Extraction of V2V Encountering Scenarios from Naturalistic Driving Database
To overcome this problem, we extract naturalistic V2V encounters data from the database, and then separate the primary vehicle encounter category by clustering.
