3,704 research outputs found
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
Data-driven System Identification and Optimal Control Framework for Grand-Prix Style Autonomous Racing
For the past 30 years, autonomous driving has witnessed a tremendous improvements thanks to the surge of computing power. Not only did we witness the autonomous vehicle navigate itself safely in the urban area, stories about more diverse autonomous driving applications, such as off-road rally-style navigation, are also commonly mentioned. Just until recently, the exponential increase in GPU and high-performance computing technology has motivated the research on autonomous driving under extreme situations such as autonomous racing or drifting.[25] The motivation for this thesis is to offer a brief overview about the main challenge of autonomous driving control and planning in racing scenario along with the potential solutions.
The first contribution is using koopmam operator and deep neural network to perform data-driven system identification. We then design optimal model-based control which is based on the learned dynamics alone. Based on our new system identification algorithm, we can approximate an accurate, explainable, and linearized system representation in a high-dimensional latent space, without any prior knowledge of the system. In this case, the learned vehicle dynamic automatically involves the information that is normally difficult to obtain, including cornering stiffness, tire slip, transmission parameters, etc. Our result shows that our koopman data-driven optimal control approach is able to deliver better tracking accuracy at high speed compared to the state-of-art vehicle controllers.
The second contribution is an iterative learning and sampling algorithm that can perform minimum-time optimization of the global racing trajectory(aka racing line) within the limit of tire friction. This trajectory optimization algorithm is not only proven to be computationally efficient, but also safe enough for the onboard RC vehicle’s test.
The research achievements we made for the last two years not only enables the F1TENTH racing team of Clemson University Mechanical Engineering Department to finish top 5 in both virtual autonomous racing hosted by IFAC and IROS congress, but also offer us the opportunity to join ICRA 2021 Autonomous racing workshop to present our work and being awarded the joint best paper. More importantly, these contributions proved to be functional and effective in the on-board testing of the real F1TENTH robot’s autonomous navigation in the Flour Danial basement. Finally, this thesis will also include discussions of the potential research directions that can help improve the our current method so that it can better contribute to the autonomous driving industry
Hierarchical Control for Cooperative Teams in Competitive Autonomous Racing
We investigate the problem of autonomous racing among teams of cooperative
agents that are subject to realistic racing rules. Our work extends previous
research on hierarchical control in head-to-head autonomous racing by
considering a generalized version of the problem while maintaining the
two-level hierarchical control structure. A high-level tactical planner
constructs a discrete game that encodes the complex rules using simplified
dynamics to produce a sequence of target waypoints. The low-level path planner
uses these waypoints as a reference trajectory and computes high-resolution
control inputs by solving a simplified formulation of a racing game with a
simplified representation of the realistic racing rules. We explore two
approaches for the low-level path planner: training a multi-agent reinforcement
learning (MARL) policy and solving a linear-quadratic Nash game (LQNG)
approximation. We evaluate our controllers on simple and complex tracks against
three baselines: an end-to-end MARL controller, a MARL controller tracking a
fixed racing line, and an LQNG controller tracking a fixed racing line.
Quantitative results show our hierarchical methods outperform the baselines in
terms of race wins, overall team performance, and compliance with the rules.
Qualitatively, we observe the hierarchical controllers mimic actions performed
by expert human drivers such as coordinated overtaking, defending against
multiple opponents, and long-term planning for delayed advantages
Winning the 3rd Japan Automotive AI Challenge -- Autonomous Racing with the Autoware.Auto Open Source Software Stack
The 3rd Japan Automotive AI Challenge was an international online autonomous
racing challenge where 164 teams competed in December 2021. This paper outlines
the winning strategy to this competition, and the advantages and challenges of
using the Autoware.Auto open source autonomous driving platform for multi-agent
racing. Our winning approach includes a lane-switching opponent overtaking
strategy, a global raceline optimization, and the integration of various tools
from Autoware.Auto including a Model-Predictive Controller. We describe the use
of perception, planning and control modules for high-speed racing applications
and provide experience-based insights on working with Autoware.Auto. While our
approach is a rule-based strategy that is suitable for non-interactive
opponents, it provides a good reference and benchmark for learning-enabled
approaches.Comment: Accepted at Autoware Workshop at IV 202
A predictive safety filter for learning-based racing control
The growing need for high-performance controllers in safety-critical
applications like autonomous driving has been motivating the development of
formal safety verification techniques. In this paper, we design and implement a
predictive safety filter that is able to maintain vehicle safety with respect
to track boundaries when paired alongside any potentially unsafe control
signal, such as those found in learning-based methods. A model predictive
control (MPC) framework is used to create a minimally invasive algorithm that
certifies whether a desired control input is safe and can be applied to the
vehicle, or that provides an alternate input to keep the vehicle in bounds. To
this end, we provide a principled procedure to compute a safe and invariant set
for nonlinear dynamic bicycle models using efficient convex approximation
techniques. To fully support an aggressive racing performance without
conservative safety interventions, the safe set is extended in real-time
through predictive control backup trajectories. Applications for assisted
manual driving and deep imitation learning on a miniature remote-controlled
vehicle demonstrate the safety filter's ability to ensure vehicle safety during
aggressive maneuvers
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