Social interactions for autonomous driving: A review and perspectives
No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …
their goals in social traffic scenes. A rational human driver can interact with other road users …
Pedestrian trajectory prediction in pedestrian-vehicle mixed environments: A systematic review
M Golchoubian, M Ghafurian… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires
reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction …
reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving
Autonomous vehicles operating in complex real-world environments require accurate
predictions of interactive behaviors between traffic participants. This paper tackles the …
predictions of interactive behaviors between traffic participants. This paper tackles the …
Hdgt: Heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding
Encoding a driving scene into vector representations has been an essential task for
autonomous driving that can benefit downstream tasks eg, trajectory prediction. The driving …
autonomous driving that can benefit downstream tasks eg, trajectory prediction. The driving …
[HTML][HTML] Toward human-in-the-loop AI: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving
Due to its limited intelligence and abilities, machine learning is currently unable to handle
various situations thus cannot completely replace humans in real-world applications …
various situations thus cannot completely replace humans in real-world applications …
Uncertainty-aware model-based reinforcement learning: Methodology and application in autonomous driving
To further improve learning efficiency and performance of reinforcement learning (RL), a
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …
Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving
Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …
Multi-modal motion prediction with transformer-based neural network for autonomous driving
Predicting the behaviors of other agents on the road is critical for autonomous driving to
ensure safety and efficiency. However, the challenging part is how to represent the social …
ensure safety and efficiency. However, the challenging part is how to represent the social …
Gorela: Go relative for viewpoint-invariant motion forecasting
The task of motion forecasting is critical for self-driving vehicles (SDV s) to be able to plan a
safe maneuver. Towards this goal, modern approaches reason about the map, the agents' …
safe maneuver. Towards this goal, modern approaches reason about the map, the agents' …