6,311 research outputs found
RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks
A key aspect of driving a road vehicle is to interact with the other road
users, assess their intentions and make risk-aware tactical decisions. An
intuitive approach of enabling an intelligent automated driving system would be
to incorporate some aspects of the human driving behavior. To this end, we
propose a novel driving framework for egocentric views, which is based on
spatio-temporal traffic graphs. The traffic graphs not only model the spatial
interactions amongst the road users, but also their individual intentions
through temporally associated message passing. We leverage spatio-temporal
graph convolutional network (ST-GCN) to train the graph edges. These edges are
formulated using parameterized functions of 3D positions and scene-aware
appearance features of road agents. Along with tactical behavior prediction, it
is crucial to evaluate the risk assessing ability of the proposed framework. We
claim that our framework learns risk aware representations by improving on the
task of risk object identification, especially in identifying objects with
vulnerable interactions like pedestrians and cyclists
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning
The widespread adoption of commercial autonomous vehicles (AVs) and advanced
driver assistance systems (ADAS) may largely depend on their acceptance by
society, for which their perceived trustworthiness and interpretability to
riders are crucial. In general, this task is challenging because modern
autonomous systems software relies heavily on black-box artificial intelligence
models. Towards this goal, this paper introduces a novel dataset, Rank2Tell, a
multi-modal ego-centric dataset for Ranking the importance level and Telling
the reason for the importance. Using various close and open-ended visual
question answering, the dataset provides dense annotations of various semantic,
spatial, temporal, and relational attributes of various important objects in
complex traffic scenarios. The dense annotations and unique attributes of the
dataset make it a valuable resource for researchers working on visual scene
understanding and related fields. Further, we introduce a joint model for joint
importance level ranking and natural language captions generation to benchmark
our dataset and demonstrate performance with quantitative evaluations
Driver-centric Risk Object Identification
A massive number of traffic fatalities are due to driver errors. To reduce
fatalities, developing intelligent driving systems assisting drivers to
identify potential risks is in urgent need. Risky situations are generally
defined based on collision prediction in existing research. However, collisions
are only one type of risk in traffic scenarios. We believe a more generic
definition is required. In this work, we propose a novel driver-centric
definition of risk, i.e., risky objects influence driver behavior. Based on
this definition, a new task called risk object identification is introduced. We
formulate the task as a cause-effect problem and present a novel two-stage risk
object identification framework, taking inspiration from models of situation
awareness and causal inference. A driver-centric Risk Object Identification
(ROI) dataset is curated to evaluate the proposed system. We demonstrate
state-of-the-art risk object identification performance compared with strong
baselines on the ROI dataset. In addition, we conduct extensive ablative
studies to justify our design choices.Comment: Submitted to TPAM
Behavioral Intention Prediction in Driving Scenes: A Survey
In the driving scene, the road agents usually conduct frequent interactions
and intention understanding of the surroundings. Ego-agent (each road agent
itself) predicts what behavior will be engaged by other road users all the time
and expects a shared and consistent understanding for safe movement. Behavioral
Intention Prediction (BIP) simulates such a human consideration process and
fulfills the early prediction of specific behaviors. Similar to other
prediction tasks, such as trajectory prediction, data-driven deep learning
methods have taken the primary pipeline in research. The rapid development of
BIP inevitably leads to new issues and challenges. To catalyze future research,
this work provides a comprehensive review of BIP from the available datasets,
key factors and challenges, pedestrian-centric and vehicle-centric BIP
approaches, and BIP-aware applications. Based on the investigation, data-driven
deep learning approaches have become the primary pipelines. The behavioral
intention types are still monotonous in most current datasets and methods
(e.g., Crossing (C) and Not Crossing (NC) for pedestrians and Lane Changing
(LC) for vehicles) in this field. In addition, for the safe-critical scenarios
(e.g., near-crashing situations), current research is limited. Through this
investigation, we identify open issues in behavioral intention prediction and
suggest possible insights for future research.Comment: 254 reference
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