Review of pedestrian trajectory prediction methods: Comparing deep learning and knowledge-based approaches
In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task
depending on many external factors. The topology of the scene and the interactions …
depending on many external factors. The topology of the scene and the interactions …
Modeling pedestrian behavior in pedestrian-vehicle near misses: A continuous Gaussian Process Inverse Reinforcement Learning (GP-IRL) approach
Using simulation models to conduct safety assessments can have several advantages as it
enables the evaluation of the safety of various design and traffic management options before …
enables the evaluation of the safety of various design and traffic management options before …
Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and demonstrations
Socially assistive robots have the potential to augment and enhance therapist's
effectiveness in repetitive tasks such as cognitive therapies. However, their contribution has …
effectiveness in repetitive tasks such as cognitive therapies. However, their contribution has …
Multiagent modeling of pedestrian-vehicle conflicts using Adversarial Inverse Reinforcement Learning
There is a need for a better understanding of the collision avoidance behavior of road users
in near misses. Recently, several models of road user behavior in near misses have been …
in near misses. Recently, several models of road user behavior in near misses have been …
Modeling crossing behaviors of E-bikes at intersection with deep maximum entropy inverse reinforcement learning using drone-based video data
The crossing behaviors modeling of non-networked road users can improve Connected and
Autonomous Vehicles (CAVs)'s awareness of the imminent hazards in shared space while …
Autonomous Vehicles (CAVs)'s awareness of the imminent hazards in shared space while …
Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review
Policy learning enables agents to learn how to map states to actions, thus enabling adaptive
and flexible behavioral generation in complex environments. Policy learning methods are …
and flexible behavioral generation in complex environments. Policy learning methods are …
[HTML][HTML] HUM-CARD: A human crowded annotated real dataset
G Di Gennaro, C Greco, A Buonanno, M Cuciniello… - Information Systems, 2024 - Elsevier
The growth of data-driven approaches typical of Machine Learning leads to an ever-
increasing need for large quantities of labeled data. Unfortunately, these attributions are …
increasing need for large quantities of labeled data. Unfortunately, these attributions are …
Research on 3D ground penetrating radar deep underground cavity identification algorithm in urban roads using multi-dimensional time-frequency features
F Li, F Yang, Y **e, X Qiao, C Du, C Li, Q Ru… - NDT & E …, 2024 - Elsevier
The 3D ground penetrating radar (GPR) is the main method for detecting underground
cavities in urban roads. Due to the weak reflected signal energy of deep road cavities with …
cavities in urban roads. Due to the weak reflected signal energy of deep road cavities with …
A generalised inverse reinforcement learning framework
The gloabal objective of inverse Reinforcement Learning (IRL) is to estimate the unknown
cost function of some MDP base on observed trajectories generated by (approximate) …
cost function of some MDP base on observed trajectories generated by (approximate) …
Identifying the automated vehicle's driving policy in the vicinity of pedestrians
The era of automation has already been launched in the field of transportation, expected to
increase road capacity and safety levels by reducing and eliminating crashes while the …
increase road capacity and safety levels by reducing and eliminating crashes while the …