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Found 7 papers, 3 papers with code
Situation Awareness for Driver-Centric Driving Style Adaptation
Furthermore, we found that feature encoders pretrained on our dataset lead to more precise driving behavior modeling.
Steering Feedback in Dynamic Driving Simulators: The Influence of Steering Wheel Vibration and Vehicle Motion Frequency
The statistical analysis of subjective results show that there is a significant influence of the frequency content of both steering wheel torque and vehicle motion on the subjective evaluation of steering feedback in a dynamic driving simulator.
Exploring the Influence of Driving Context on Lateral Driving Style Preferences: A Simulator-Based Study
Despite the increased research focus on driving styles, there remains a need for comprehensive studies investigating how variations in the driving context impact the assessment of automated driving functions.
Self-Perception Versus Objective Driving Behavior: Subject Study of Lateral Vehicle Guidance
Ensuring a safe and comfortable ride experience is vital for the widespread adoption of autonomous vehicles, as mismatches in driving styles between humans and autonomous systems can impact passenger confidence.
PanopticNDT: Efficient and Robust Panoptic Mapping
As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial.
Ranked #2 on
Semantic Segmentation
on ScanNetV2
Vectorized Scenario Description and Motion Prediction for Scenario-Based Testing
Automated vehicles (AVs) are tested in diverse scenarios, typically specified by parameters such as velocities, distances, or curve radii.
Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios
However, despite the safety criticality of AV testing, metamodels are usually seen as a part of an overall approach, and their predictions are not questioned.
