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 …
Stepwise goal-driven networks for trajectory prediction
We propose to predict the future trajectories of observed agents (eg, pedestrians or vehicles)
by estimating and using their goals at multiple time scales. We argue that the goal of a …
by estimating and using their goals at multiple time scales. We argue that the goal of a …
Bifold and semantic reasoning for pedestrian behavior prediction
Pedestrian behavior prediction is one of the major challenges for intelligent driving systems.
Pedestrians often exhibit complex behaviors influenced by various contextual elements. To …
Pedestrians often exhibit complex behaviors influenced by various contextual elements. To …
On exposing the challenging long tail in future prediction of traffic actors
Predicting the future states of dynamic traffic actors enables autonomous systems to avoid
accidents and operate safely. Remarkably, the most critical scenarios are much less …
accidents and operate safely. Remarkably, the most critical scenarios are much less …
[PDF][PDF] Multimodal Transformer Networks for Pedestrian Trajectory Prediction.
We consider the problem of forecasting the future locations of pedestrians in an ego-centric
view of a moving vehicle. Current CNNs or RNNs are flawed in capturing the high dynamics …
view of a moving vehicle. Current CNNs or RNNs are flawed in capturing the high dynamics …
NAST: Non-autoregressive spatial-temporal transformer for time series forecasting
Although Transformer has made breakthrough success in widespread domains especially in
Natural Language Processing (NLP), applying it to time series forecasting is still a great …
Natural Language Processing (NLP), applying it to time series forecasting is still a great …
PedFormer: Pedestrian behavior prediction via cross-modal attention modulation and gated multitask learning
A Rasouli, I Kotseruba - 2023 IEEE International Conference on …, 2023 - ieeexplore.ieee.org
Predicting pedestrian behavior is a crucial task for intelligent driving systems. Accurate
predictions require a deep understanding of various contextual elements that could impact …
predictions require a deep understanding of various contextual elements that could impact …
A novel benchmarking paradigm and a scale-and motion-aware model for egocentric pedestrian trajectory prediction
A Rasouli - 2024 IEEE International Conference on Robotics …, 2024 - ieeexplore.ieee.org
In this paper, we present a new paradigm for evaluating egocentric pedestrian trajectory
prediction algorithms. Based on various contextual information, we extract driving scenarios …
prediction algorithms. Based on various contextual information, we extract driving scenarios …
Egocentric human trajectory forecasting with a wearable camera and multi-modal fusion
In this letter, we address the problem of forecasting the trajectory of an egocentric camera
wearer (ego-person) in crowded spaces. The trajectory forecasting ability learned from the …
wearer (ego-person) in crowded spaces. The trajectory forecasting ability learned from the …
Pedestrian trajectory prediction via spatial interaction transformer network
T Su, Y Meng, Y Xu - 2021 IEEE Intelligent Vehicles …, 2021 - ieeexplore.ieee.org
As a core technology of the autonomous driving system, pedestrian trajectory prediction can
significantly enhance the function of active vehicle safety and reduce road traffic injuries. In …
significantly enhance the function of active vehicle safety and reduce road traffic injuries. In …