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 …
Deep learning-based vehicle behavior prediction for autonomous driving applications: A review
S Mozaffari, OY Al-Jarrah, M Dianati… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Behaviour prediction function of an autonomous vehicle predicts the future states of the
nearby vehicles based on the current and past observations of the surrounding environment …
nearby vehicles based on the current and past observations of the surrounding environment …
Safety-enhanced autonomous driving using interpretable sensor fusion transformer
Large-scale deployment of autonomous vehicles has been continually delayed due to safety
concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of …
concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of …
Prediction-uncertainty-aware decision-making for autonomous vehicles
Motion prediction is the fundamental input for decision-making in autonomous vehicles. The
current motion prediction solutions are designed with a strong reliance on black box …
current motion prediction solutions are designed with a strong reliance on black box …
A review and comparative study on probabilistic object detection in autonomous driving
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …
recent years, deep learning has become the de-facto approach for object detection, and …
Intentnet: Learning to predict intention from raw sensor data
In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other
traffic participants. We define intent as a combination of discrete high level behaviors as well …
traffic participants. We define intent as a combination of discrete high level behaviors as well …
Overcoming limitations of mixture density networks: A sampling and fitting framework for multimodal future prediction
Future prediction is a fundamental principle of intelligence that helps plan actions and avoid
possible dangers. As the future is uncertain to a large extent, modeling the uncertainty and …
possible dangers. As the future is uncertain to a large extent, modeling the uncertainty and …
AI-TP: Attention-based interaction-aware trajectory prediction for autonomous driving
K Zhang, L Zhao, C Dong, L Wu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Despite the advancements in the technologies of autonomous driving, it is still challenging to
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …
Spagnn: Spatially-aware graph neural networks for relational behavior forecasting from sensor data
In this paper, we tackle the problem of relational behavior forecasting from sensor data.
Towards this goal, we propose a novel spatially-aware graph neural network (SpAGNN) that …
Towards this goal, we propose a novel spatially-aware graph neural network (SpAGNN) that …
Milestones in autonomous driving and intelligent vehicles—part ii: Perception and planning
A growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their
promise for enhanced safety, efficiency, and economic benefits. While previous surveys …
promise for enhanced safety, efficiency, and economic benefits. While previous surveys …