89 research outputs found
Minimum Race-Time Planning-Strategy for an Autonomous Electric Racecar
Increasing attention to autonomous passenger vehicles has also attracted
interest in an autonomous racing series. Because of this, platforms such as
Roborace and the Indy Autonomous Challenge are currently evolving. Electric
racecars face the challenge of a limited amount of stored energy within their
batteries. Furthermore, the thermodynamical influence of an all-electric
powertrain on the race performance is crucial. Severe damage can occur to the
powertrain components when thermally overstressed. In this work we present a
race-time minimal control strategy deduced from an Optimal Control Problem
(OCP) that is transcribed into a Nonlinear Problem (NLP). Its optimization
variables stem from the driving dynamics as well as from a thermodynamical
description of the electric powertrain. We deduce the necessary first-order
Ordinary Differential Equations (ODE)s and form simplified loss models for the
implementation within the numerical optimization. The significant influence of
the powertrain behavior on the race strategy is shown.Comment: Accepted at The 23rd IEEE International Conference on Intelligent
Transportation Systems, September 20 - 23, 202
Energy Management Strategy for an Autonomous Electric Racecar using Optimal Control
The automation of passenger vehicles is becoming more and more widespread,
leading to full autonomy of cars within the next years. Furthermore,
sustainable electric mobility is gaining in importance. As racecars have been a
development platform for technology that has later also been transferred to
passenger vehicles, a race format for autonomous electric racecars called
Roborace has been created. As electric racecars only store a limited amount of
energy, an Energy Management Strategy (EMS) is needed to work out the time as
well as the minimum energy trajectories for the track. At the same time, the
technical limitations and component behavior in the electric powertrain must be
taken into account when calculating the race trajectories. In this paper, we
present a concept for a special type of EMS. This is based on the Optimal
Control Problem (OCP) of generating a time-minimal global trajectory which is
solved by the transcription via direct orthogonal collocation to a Nonlinear
Programming Problem (NLPP). We extend this minimum lap time problem by adding
our ideas for a holistic EMS. This approach proves the fundamental feasibility
of the stated ideas, e.g. varying racepaths and velocities due to energy
limitations, covered by the EMS. Also, the presented concept forms the basis
for future work on meta-models of the powertrain's components that can be fed
into the OCP to increase the validity of the control output of the EMS.Comment: Accepted at the IEEE Intelligent Transportation Systems Conference -
ITSC 2019, Auckland, New Zealand 27 - 30 Octobe
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
er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds
The Indy Autonomous Challenge (IAC) brought together for the first time in
history nine autonomous racing teams competing at unprecedented speed and in
head-to-head scenario, using independently developed software on open-wheel
racecars. This paper presents the complete software architecture used by team
TII EuroRacing (TII-ER), covering all the modules needed to avoid static
obstacles, perform active overtakes and reach speeds above 75 m/s (270 km/h).
In addition to the most common modules related to perception, planning, and
control, we discuss the approaches used for vehicle dynamics modelling,
simulation, telemetry, and safety. Overall results and the performance of each
module are described, as well as the lessons learned during the first two
events of the competition on oval tracks, where the team placed respectively
second and third.Comment: Preprint: Accepted to Field Robotics "Opportunities and Challenges
with Autonomous Racing" Special Issu
A hierarchical autonomous driver for a racing car: Real-time planning and tracking of the trajectory
The aim of this study was to develop trajectory planning that would allow an autonomous racing car to be driven as close as possible to what a driver would do, defining the most appropriate inputs for the current scenario. The search for the optimal trajectory in terms of lap time reduction involves the modeling of all the non-linearities of the vehicle dynamics with the disadvantage of being a time-consuming problem and not being able to be implemented in real-time. However, to improve the vehicle performances, the trajectory needs to be optimized online with the knowledge of the actual vehicle dynamics and path conditions. Therefore, this study involved the development of an architecture that allows an autonomous racing car to have an optimal online trajectory planning and path tracking ensuring professional driver performances. The real-time trajectory optimization can also ensure a possible future implementation in the urban area where obstacles and dynamic scenarios could be faced. It was chosen to implement a local trajectory planning based on the Model Predictive Control(MPC) logic and solved as Linear Programming (LP) by Sequential Convex Programming (SCP). The idea was to achieve a computational cost, 0.1 s, using a point mass vehicle model constrained by experimental definition and approximation of the car’s GG-V, and developing an optimum model-based path tracking to define the driver model that allows A car to follow the trajectory defined by the planner ensuring a signal input every 0.001 s. To validate the algorithm, two types of tests were carried out: a Matlab-Simulink, Vi-Grade co-simulation test, comparing the proposed algorithm with the performance of an offline motion planning, and a real-time simulator test, comparing the proposed algorithm with the performance of a professional driver. The results obtained showed that the computational cost of the optimization algorithm developed is below the limit of 0.1 s, and the architecture showed a reduction of the lap time of about 1 s compared to the offline optimizer and reproducibility of the performance obtained by the driver
Enhancing State Estimator for Autonomous Race Car : Leveraging Multi-modal System and Managing Computing Resources
This paper introduces an innovative approach to enhance the state estimator
for high-speed autonomous race cars, addressing challenges related to
unreliable measurements, localization failures, and computing resource
management. The proposed robust localization system utilizes a Bayesian-based
probabilistic approach to evaluate multimodal measurements, ensuring the use of
credible data for accurate and reliable localization, even in harsh racing
conditions. To tackle potential localization failures during intense racing, we
present a resilient navigation system. This system enables the race car to
continue track-following by leveraging direct perception information in
planning and execution, ensuring continuous performance despite localization
disruptions. Efficient computing resource management is critical to avoid
overload and system failure. We optimize computing resources using an efficient
LiDAR-based state estimation method. Leveraging CUDA programming and GPU
acceleration, we perform nearest points search and covariance computation
efficiently, overcoming CPU bottlenecks. Real-world and simulation tests
validate the system's performance and resilience. The proposed approach
successfully recovers from failures, effectively preventing accidents and
ensuring race car safety.Comment: arXiv admin note: text overlap with arXiv:2207.1223
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