6,889 research outputs found
VIENA2: A Driving Anticipation Dataset
Action anticipation is critical in scenarios where one needs to react before
the action is finalized. This is, for instance, the case in automated driving,
where a car needs to, e.g., avoid hitting pedestrians and respect traffic
lights. While solutions have been proposed to tackle subsets of the driving
anticipation tasks, by making use of diverse, task-specific sensors, there is
no single dataset or framework that addresses them all in a consistent manner.
In this paper, we therefore introduce a new, large-scale dataset, called
VIENA2, covering 5 generic driving scenarios, with a total of 25 distinct
action classes. It contains more than 15K full HD, 5s long videos acquired in
various driving conditions, weathers, daytimes and environments, complemented
with a common and realistic set of sensor measurements. This amounts to more
than 2.25M frames, each annotated with an action label, corresponding to 600
samples per action class. We discuss our data acquisition strategy and the
statistics of our dataset, and benchmark state-of-the-art action anticipation
techniques, including a new multi-modal LSTM architecture with an effective
loss function for action anticipation in driving scenarios.Comment: Accepted in ACCV 201
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
Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles
Interaction-aware Autonomous Driving (IAAD) is a rapidly growing field of
research that focuses on the development of autonomous vehicles (AVs) that are
capable of interacting safely and efficiently with human road users. This is a
challenging task, as it requires the autonomous vehicle to be able to
understand and predict the behaviour of human road users. In this literature
review, the current state of IAAD research is surveyed in this work. Commencing
with an examination of terminology, attention is drawn to challenges and
existing models employed for modelling the behaviour of drivers and
pedestrians. Next, a comprehensive review is conducted on various techniques
proposed for interaction modelling, encompassing cognitive methods, machine
learning approaches, and game-theoretic methods. The conclusion is reached
through a discussion of potential advantages and risks associated with IAAD,
along with the illumination of pivotal research inquiries necessitating future
exploration
How do cyclists interact with motorized vehicles at unsignalized intersections? Modeling cyclists’ yielding behavior using naturalistic data
When a cyclist\u27s path intersects with that of a motorized vehicle at an unsignalized intersection, serious conflicts may happen. In recent years, the number of cyclist fatalities in this conflict scenario has held steady, while the number in many other traffic scenarios has been decreasing. There is, therefore, a need to further study this conflict scenario in order to make it safer. With the advent of automated vehicles, threat assessment algorithms able to predict cyclists’ (other road users’) behavior will be increasingly important to ensure safety. To date, the handful of studies that have modeled the vehicle-cyclist interaction at unsignalized intersections have used kinematics (speed and location) alone without using cyclists’ behavioral cues, such as pedaling or gesturing. As a result, we do not know whether non-verbal communication (e.g., from behavioral cues) could improve model predictions. In this paper, we propose a quantitative model based on naturalistic data, which uses additional non-verbal information to predict cyclists’ crossing intentions at unsignalized intersections. Interaction events were extracted from a trajectory dataset and enriched by adding cyclists’ behavioral cues obtained from sensors. Both kinematics and cyclists’ behavioral cues (e.g., pedaling and head movement), were found to be statistically significant for predicting the cyclist\u27s yielding behavior. This research shows that adding information about the cyclists’ behavioral cues to the threat assessment algorithms of active safety systems and automated vehicles will improve safety
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
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