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Found 10 papers, 3 papers with code
Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap Time Simulation Using Machine Learning
A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or, in the case of a competition vehicle, the racing line.
ROAD: The ROad event Awareness Dataset for Autonomous Driving
We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the winners of the ICCV2021 ROAD challenge, which highlight the challenges faced by situation awareness in autonomous driving.
Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions
Autonomous vehicles rely heavily upon their perception subsystems to see the environment in which they operate.
YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles
As autonomous vehicles and autonomous racing rise in popularity, so does the need for faster and more accurate detectors.
Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing
In an autonomous driving system, perception - identification of features and objects from the environment - is crucial.
Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset
Connected and Autonomous Vehicles (CAVs) rely on Vehicular Adhoc Networks with wireless communication between vehicles and roadside infrastructure to support safe operation.
Never mind the metrics -- what about the uncertainty? Visualising confusion matrix metric distributions
There are strong incentives to build models that demonstrate outstanding predictive performance on various datasets and benchmarks.
A Scenario-Based Functional Testing Approach to Improving DNN Performance
This paper proposes a scenario-based functional testing approach for enhancing the performance of machine learning (ML) applications.
Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction
The emerging field of action prediction plays a vital role in various computer vision applications such as autonomous driving, activity analysis and human-computer interaction.
A Hybrid Graph Network for Complex Activity Detection in Video
Attention is then applied to this graph to obtain an overall representation of the local dynamic scene.
