1,609 research outputs found
Advances in centerline estimation for autonomous lateral control
The ability of autonomous vehicles to maintain an accurate trajectory within
their road lane is crucial for safe operation. This requires detecting the road
lines and estimating the car relative pose within its lane. Lateral lines are
usually retrieved from camera images. Still, most of the works on line
detection are limited to image mask retrieval and do not provide a usable
representation in world coordinates. What we propose in this paper is a
complete perception pipeline based on monocular vision and able to retrieve all
the information required by a vehicle lateral control system: road lines
equation, centerline, vehicle heading and lateral displacement. We evaluate our
system by acquiring data with accurate geometric ground truth. To act as a
benchmark for further research, we make this new dataset publicly available at
http://airlab.deib.polimi.it/datasets/.Comment: Presented at 2020 IEEE Intelligent Vehicles Symposium (IV), 8 pages,
8 figure
A Sequential Two-Step Algorithm for Fast Generation of Vehicle Racing Trajectories
The problem of maneuvering a vehicle through a race course in minimum time
requires computation of both longitudinal (brake and throttle) and lateral
(steering wheel) control inputs. Unfortunately, solving the resulting nonlinear
optimal control problem is typically computationally expensive and infeasible
for real-time trajectory planning. This paper presents an iterative algorithm
that divides the path generation task into two sequential subproblems that are
significantly easier to solve. Given an initial path through the race track,
the algorithm runs a forward-backward integration scheme to determine the
minimum-time longitudinal speed profile, subject to tire friction constraints.
With this fixed speed profile, the algorithm updates the vehicle's path by
solving a convex optimization problem that minimizes the resulting path
curvature while staying within track boundaries and obeying affine,
time-varying vehicle dynamics constraints. This two-step process is repeated
iteratively until the predicted lap time no longer improves. While providing no
guarantees of convergence or a globally optimal solution, the approach performs
very well when validated on the Thunderhill Raceway course in Willows, CA. The
predicted lap time converges after four to five iterations, with each iteration
over the full 4.5 km race course requiring only thirty seconds of computation
time on a laptop computer. The resulting trajectory is experimentally driven at
the race circuit with an autonomous Audi TTS test vehicle, and the resulting
lap time and racing line is comparable to both a nonlinear gradient descent
solution and a trajectory recorded from a professional racecar driver. The
experimental results indicate that the proposed method is a viable option for
online trajectory planning in the near future
Imitating Driver Behavior with Generative Adversarial Networks
The ability to accurately predict and simulate human driving behavior is
critical for the development of intelligent transportation systems. Traditional
modeling methods have employed simple parametric models and behavioral cloning.
This paper adopts a method for overcoming the problem of cascading errors
inherent in prior approaches, resulting in realistic behavior that is robust to
trajectory perturbations. We extend Generative Adversarial Imitation Learning
to the training of recurrent policies, and we demonstrate that our model
outperforms rule-based controllers and maximum likelihood models in realistic
highway simulations. Our model both reproduces emergent behavior of human
drivers, such as lane change rate, while maintaining realistic control over
long time horizons.Comment: 8 pages, 6 figure
SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving
Autonomous driving confronts great challenges in complex traffic scenarios,
where the risk of Safety of the Intended Functionality (SOTIF) can be triggered
by the dynamic operational environment and system insufficiencies. The SOTIF
risk is reflected not only intuitively in the collision risk with objects
outside the autonomous vehicles (AVs), but also inherently in the performance
limitation risk of the implemented algorithms themselves. How to minimize the
SOTIF risk for autonomous driving is currently a critical, difficult, and
unresolved issue. Therefore, this paper proposes the "Self-Surveillance and
Self-Adaption System" as a systematic approach to online minimize the SOTIF
risk, which aims to provide a systematic solution for monitoring,
quantification, and mitigation of inherent and external risks. The core of this
system is the risk monitoring of the implemented artificial intelligence
algorithms within the AV. As a demonstration of the Self-Surveillance and
Self-Adaption System, the risk monitoring of the perception algorithm, i.e.,
YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and
external collision risk are jointly quantified via SOTIF entropy, which is then
propagated downstream to the decision-making module and mitigated. Finally,
several challenging scenarios are demonstrated, and the Hardware-in-the-Loop
experiments are conducted to verify the efficiency and effectiveness of the
system. The results demonstrate that the Self-Surveillance and Self-Adaption
System enables dependable online monitoring, quantification, and mitigation of
SOTIF risk in real-time critical traffic environments.Comment: 16 pages, 10 figures, 2 tables, submitted to IEEE TIT
Quantifying Assurance in Learning-enabled Systems
Dependability assurance of systems embedding machine learning(ML)
components---so called learning-enabled systems (LESs)---is a key step for
their use in safety-critical applications. In emerging standardization and
guidance efforts, there is a growing consensus in the value of using assurance
cases for that purpose. This paper develops a quantitative notion of assurance
that an LES is dependable, as a core component of its assurance case, also
extending our prior work that applied to ML components. Specifically, we
characterize LES assurance in the form of assurance measures: a probabilistic
quantification of confidence that an LES possesses system-level properties
associated with functional capabilities and dependability attributes. We
illustrate the utility of assurance measures by application to a real world
autonomous aviation system, also describing their role both in i) guiding
high-level, runtime risk mitigation decisions and ii) as a core component of
the associated dynamic assurance case.Comment: Author's pre-print version of manuscript accepted for publication in
the Proceedings of the 39th International Conference in Computer Safety,
Reliability, and Security (SAFECOMP 2020
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