9,016 research outputs found
The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?
While driving on highways, every driver tries to be aware of the behavior of
surrounding vehicles, including possible emergency braking, evasive maneuvers
trying to avoid obstacles, unexpected lane changes, or other emergencies that
could lead to an accident. In this paper, human's ability to predict lane
changes in highway scenarios is analyzed through the use of video sequences
extracted from the PREVENTION dataset, a database focused on the development of
research on vehicle intention and trajectory prediction. Thus, users had to
indicate the moment at which they considered that a lane change maneuver was
taking place in a target vehicle, subsequently indicating its direction: left
or right. The results retrieved have been carefully analyzed and compared to
ground truth labels, evaluating statistical models to understand whether humans
can actually predict. The study has revealed that most participants are unable
to anticipate lane-change maneuvers, detecting them after they have started.
These results might serve as a baseline for AI's prediction ability evaluation,
grading if those systems can outperform human skills by analyzing hidden cues
that seem unnoticed, improving the detection time, and even anticipating
maneuvers in some cases.Comment: This work was accepted and presented at IEEE Intelligent Vehicles
Symposium 202
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
Advanced Driver Assistance Systems (ADAS) have made driving safer over the
last decade. They prepare vehicles for unsafe road conditions and alert drivers
if they perform a dangerous maneuver. However, many accidents are unavoidable
because by the time drivers are alerted, it is already too late. Anticipating
maneuvers beforehand can alert drivers before they perform the maneuver and
also give ADAS more time to avoid or prepare for the danger.
In this work we anticipate driving maneuvers a few seconds before they occur.
For this purpose we equip a car with cameras and a computing device to capture
the driving context from both inside and outside of the car. We propose an
Autoregressive Input-Output HMM to model the contextual information alongwith
the maneuvers. We evaluate our approach on a diverse data set with 1180 miles
of natural freeway and city driving and show that we can anticipate maneuvers
3.5 seconds before they occur with over 80\% F1-score in real-time.Comment: ICCV 2015, http://brain4cars.co
Measuring Sociality in Driving Interaction
Interacting with other human road users is one of the most challenging tasks
for autonomous vehicles. For congruent driving behaviors, it is essential to
recognize and comprehend sociality, encompassing both implicit social norms and
individualized social preferences of human drivers. To understand and quantify
the complex sociality in driving interactions, we propose a Virtual-Game-based
Interaction Model (VGIM) that is parameterized by a social preference
measurement, Interaction Preference Value (IPV). The IPV is designed to capture
the driver's relative inclination towards individual rewards over group
rewards. A method for identifying IPV from observed driving trajectory is also
developed, with which we assessed human drivers' IPV using driving data
recorded in a typical interactive driving scenario, the unprotected left turn.
Our findings reveal that (1) human drivers exhibit particular social preference
patterns while undertaking specific tasks, such as turning left or proceeding
straight; (2) competitive actions could be strategically conducted by human
drivers in order to coordinate with others. Finally, we discuss the potential
of learning sociality-aware navigation from human demonstrations by
incorporating a rule-based humanlike IPV expressing strategy into VGIM and
optimization-based motion planners. Simulation experiments demonstrate that (1)
IPV identification improves the motion prediction performance in interactive
driving scenarios and (2) the dynamic IPV expressing strategy extracted from
human driving data makes it possible to reproduce humanlike coordination
patterns in the driving interaction
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Interactive Prediction and Planning for Autonomous Driving: from Algorithms to Fundamental Aspects
Inevitably, autonomous vehicles need to interact with other road participants in a variety of highly complex or critical driving scenarios. It is still an extremely challenging task even for the forefront companies or institutes to enable autonomous vehicles to interactively predict the behavior of others, and plan safe and high-quality motions accordingly. The major obstacles are not just originated from prediction and planning algorithms with insufficient performances. Several fundamental problems in the fields of interactive prediction and planning still remain open, such as formulation, representation and evaluation of interactive prediction methods, motion dataset with densely interactive driving behavior, as well as interface of interactive prediction and planning algorithms. The aforementioned fundamental aspects of interactive prediction and planning are addressed in this dissertation along with various kinds of algorithms. First, generic environmental representation for various scenarios with topological decomposition is constructed, and a corresponding planning algorithm is designed by combining graph search and optimization. Hard constraints in optimization-based planners are also incorporated into the training loss of imitation learning so that the policy net can generate safe and feasible motions in highly constrained scenarios. Unified problem formulation and motion representation are designed for different paradigms of interactive predictors such as planning-based prediction (inverse reinforcement learning), as well as probabilistic graphical models (hidden Markov model) and deep neural networks (mixture density network), which are utilized for the prediction/planning interface design and prediction benchmark. A framework combing decision network and graph-search/optimization/sample-based planner is proposed to achieve a driving strategy which is defensive to potential violations of others, but not overly conservatively to threats of low probabilities. Such driving strategy is achieved via experiments based on the aforementioned interactive prediction and planning algorithms with proper interface designed. These predictors are also evaluated from closed loop perspective considering planning fatality when using the prediction results instead of pure data approximation metrics. Finally, INTERACTION (INTERnational, Adversarial and Cooperative moTION) dataset with highly interactive driving scenarios and behavior from international locations is constructed with interaction density metric defined to compare different datasets. The dataset has been utilized for various behavior-related research areas such as prediction, planning, imitation learning and behavior modeling, and is inspiring new research fields such as representation learning, interaction extraction and scenario generation
Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior
Connected and automated vehicles (CAVs) are supposed to share the road with
human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering
the mixed traffic environment is more pragmatic, as the well-planned operation
of CAVs may be interrupted by HDVs. In the circumstance that human behaviors
have significant impacts, CAVs need to understand HDV behaviors to make safe
actions. In this study, we develop a Driver Digital Twin (DDT) for the online
prediction of personalized lane change behavior, allowing CAVs to predict
surrounding vehicles' behaviors with the help of the digital twin technology.
DDT is deployed on a vehicle-edge-cloud architecture, where the cloud server
models the driver behavior for each HDV based on the historical naturalistic
driving data, while the edge server processes the real-time data from each
driver with his/her digital twin on the cloud to predict the lane change
maneuver. The proposed system is first evaluated on a human-in-the-loop
co-simulation platform, and then in a field implementation with three passenger
vehicles connected through the 4G/LTE cellular network. The lane change
intention can be recognized in 6 seconds on average before the vehicle crosses
the lane separation line, and the Mean Euclidean Distance between the predicted
trajectory and GPS ground truth is 1.03 meters within a 4-second prediction
window. Compared to the general model, using a personalized model can improve
prediction accuracy by 27.8%. The demonstration video of the proposed system
can be watched at https://youtu.be/5cbsabgIOdM
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