Search Results for author:
Found 20 papers, 7 papers with code
EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV
One white-box and two black-box patch based attack methods are implemented to attack three classic deep neural networks based object detectors on EVD4UAV.
Abductive Ego-View Accident Video Understanding for Safe Driving Perception
This model involves a contrastive interaction loss to learn the pair co-occurrence of normal, near-accident, accident frames with the corresponding text descriptions, such as accident reasons, prevention advice, and accident categories.
Vehicle Behavior Prediction by Episodic-Memory Implanted NDT
In this work, we explore the interpretability of behavior prediction of target vehicles by an Episodic Memory implanted Neural Decision Tree (abbrev.
AdvGPS: Adversarial GPS for Multi-Agent Perception Attack
The multi-agent perception system collects visual data from sensors located on various agents and leverages their relative poses determined by GPS signals to effectively fuse information, mitigating the limitations of single-agent sensing, such as occlusion.
VehicleGAN: Pair-flexible Pose Guided Image Synthesis for Vehicle Re-identification
Vehicle Re-identification (Re-ID) has been broadly studied in the last decade; however, the different camera view angle leading to confused discrimination in the feature subspace for the vehicles of various poses, is still challenging for the Vehicle Re-ID models in the real world.
Vision-Based Traffic Accident Detection and Anticipation: A Survey
We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA.
Gated Driver Attention Predictor
In this work, we explore the network connection gating mechanism for driver attention prediction (Gate-DAP).
Counterfactual Graph Transformer for Traffic Flow Prediction
We design a perturbation mask generator over input sensor features at the time dimension and the graph structure on the graph transformer module to obtain spatial and temporal counterfactual explanations.
UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment
This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method for a large-scale 3D complex environment.
ConMAE: Contour Guided MAE for Unsupervised Vehicle Re-Identification
With the large-scale and dynamic road environment, the paradigm of supervised vehicle re-identification shows limited scalability because of the heavy reliance on large-scale annotated datasets.
Self-Supervised Learning
Unsupervised Vehicle Re-Identification
+1
Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark
In particular, the text description provides a dense semantic description guidance for the primary context of the traffic scene, while the driver attention provides a traction to focus on the critical region closely correlating with safe driving.
Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning
Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraints.
Deep Virtual-to-Real Distillation for Pedestrian Crossing Prediction
To this end, we formulate a deep virtual to real distillation framework by introducing the synthetic data that can be generated conveniently, and borrow the abundant information of pedestrian movement in synthetic videos for the pedestrian crossing prediction in real data with a simple and lightweight implementation.
Behavioral Intention Prediction in Driving Scenes: A Survey
Behavioral Intention Prediction (BIP) simulates such a human consideration process and fulfills the early prediction of specific behaviors.
Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios
In this paper, we propose a lightweight system, RDS-SLAM, based on ORB-SLAM2, which can accurately estimate poses and build semantic maps at object level for dynamic scenarios in real time using only one commonly used Intel Core i7 CPU.
Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding
Trajectories obtained from object detection and tracking are inevitably noisy, which could cause serious forecasting errors to predictors built on ground truth trajectories.
DCDLearn: Multi-order Deep Cross-distance Learning for Vehicle Re-Identification
Vehicle re-identification (Re-ID) has become a popular research topic owing to its practicability in intelligent transportation systems.
DADA: Driver Attention Prediction in Driving Accident Scenarios
1) With the semantic images, we introduce their semantic context features and verified the manifest promotion effect for helping the driver attention prediction, where the semantic context features are modeled by a graph convolution network (GCN) on semantic images; 2) We fuse the semantic context features of semantic images and the features of RGB frames in an attentive strategy, and the fused details are transferred over frames by a convolutional LSTM module to obtain the attention map of each video frame with the consideration of historical scene variation in driving situations; 3) The superiority of the proposed method is evaluated on our previously collected dataset (named as DADA-2000) and two other challenging datasets with state-of-the-art methods.
DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark
Driver attention prediction is currently becoming the focus in safe driving research community, such as the DR(eye)VE project and newly emerged Berkeley DeepDrive Attention (BDD-A) database in critical situations.
BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving
Instead, BLVD aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking, 5D (4D+interactive) interactive event recognition and intention prediction.
