Behavioral intention prediction in driving scenes: A survey
In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
[HTML][HTML] Deep transfer learning for intelligent vehicle perception: A survey
Deep learning-based intelligent vehicle perception has been developing prominently in
recent years to provide a reliable source for motion planning and decision making in …
recent years to provide a reliable source for motion planning and decision making in …
Safety-assured speculative planning with adaptive prediction
Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …
Scenario-transferable semantic graph reasoning for interaction-aware probabilistic prediction
Accurately predicting the possible behaviors of traffic participants is an essential capability
for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically …
for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically …
Benchmarking behavior prediction models in gap acceptance scenarios
JF Schumann, J Kober… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Autonomous vehicles currently suffer from a time-inefficient driving style caused by
uncertainty about human behavior in traffic interactions. Accurate and reliable prediction …
uncertainty about human behavior in traffic interactions. Accurate and reliable prediction …
Attentive Transfer Entropy to Exploit Transient Emergence of Coupling Effect
X Ru, X ZHANG, Z Liu, JM Moore… - Advances in Neural …, 2024 - proceedings.neurips.cc
We consider the problem of reconstructing coupled networks (eg, biological neural
networks) connecting large numbers of variables (eg, nerve cells), of which state evolution is …
networks) connecting large numbers of variables (eg, nerve cells), of which state evolution is …
Uqnet: Quantifying uncertainty in trajectory prediction by a non-parametric and generalizable approach
Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory
prediction contains many sources of uncertainty in data and modeling. A thorough …
prediction contains many sources of uncertainty in data and modeling. A thorough …
[HTML][HTML] Unravelling uncertainty in trajectory prediction using a non-parametric approach
Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory
prediction contains many sources of uncertainty in data and modelling. A thorough …
prediction contains many sources of uncertainty in data and modelling. A thorough …
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
Estimating the potential behavior of the surrounding human-driven vehicles is crucial for the
safety of autonomous vehicles in a mixed traffic flow. Recent state-of-the-art achieved …
safety of autonomous vehicles in a mixed traffic flow. Recent state-of-the-art achieved …
Coupling LSTM and CNN Neural Networks for Accurate Carbon Emission Prediction in 30 Chinese Provinces
Z Han, B Cui, L Xu, J Wang, Z Guo - Sustainability, 2023 - mdpi.com
Global warming is a major environmental issue facing humanity, and the resulting climate
change has severely affected the environment and daily lives of people. China attaches …
change has severely affected the environment and daily lives of people. China attaches …