193 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
Benchmarking of a software stack for autonomous racing against a professional human race driver
The way to full autonomy of public road vehicles requires the step-by-step
replacement of the human driver, with the ultimate goal of replacing the driver
completely. Eventually, the driving software has to be able to handle all
situations that occur on its own, even emergency situations. These particular
situations require extreme combined braking and steering actions at the limits
of handling to avoid an accident or to diminish its consequences. An average
human driver is not trained to handle such extreme and rarely occurring
situations and therefore often fails to do so. However, professional race
drivers are trained to drive a vehicle utilizing the maximum amount of possible
tire forces. These abilities are of high interest for the development of
autonomous driving software. Here, we compare a professional race driver and
our software stack developed for autonomous racing with data analysis
techniques established in motorsports. The goal of this research is to derive
indications for further improvement of the performance of our software and to
identify areas where it still fails to meet the performance level of the human
race driver. Our results are used to extend our software's capabilities and
also to incorporate our findings into the research and development of public
road autonomous vehicles.Comment: Accepted at 2020 Fifteenth International Conference on Ecological
Vehicles and Renewable Energies (EVER
MixNet: Structured Deep Neural Motion Prediction for Autonomous Racing
Reliably predicting the motion of contestant vehicles surrounding an
autonomous racecar is crucial for effective and performant planning. Although
highly expressive, deep neural networks are black-box models, making their
usage challenging in safety-critical applications, such as autonomous driving.
In this paper, we introduce a structured way of forecasting the movement of
opposing racecars with deep neural networks. The resulting set of possible
output trajectories is constrained. Hence quality guarantees about the
prediction can be given. We report the performance of the model by evaluating
it together with an LSTM-based encoder-decoder architecture on data acquired
from high-fidelity Hardware-in-the-Loop simulations. The proposed approach
outperforms the baseline regarding the prediction accuracy but still fulfills
the quality guarantees. Thus, a robust real-world application of the model is
proven. The presented model was deployed on the racecar of the Technical
University of Munich for the Indy Autonomous Challenge 2021. The code used in
this research is available as open-source software at
www.github.com/TUMFTM/MixNet
Design and Implementation of Sensing Methods on One-Tenth Scale of an Autonomous Race Car
Indiana University-Purdue University Indianapolis (IUPUI)Self-driving is simply the capacity of a vehicle to drive itself without human intervention. To accomplish this, the vehicle utilizes mechanical and electronic parts, sensors, actuators and an AI computer. The on-board PC runs advanced programming, which permits the vehicle to see and comprehend its current circumstance dependent on sensor input, limit itself in that climate and plan the ideal course from point A to point B. Independent driving is not an easy task, and to create self-sufficient driving arrangements is an exceptionally significant ability in the present programming designing field.
ROS is a robust and versatile communication middle ware (framework) tailored and widely used for robotics applications. This thesis work intends to show how ROS could be used to create independent driving programming by investigating self-governing driving issues, looking at existing arrangements and building up a model vehicle utilizing ROS.
The main focus of this thesis is to develop and implement a one-tenth scale of an autonomous RACECAR equipped with Jetson Nano board as the on-board computer, PCA9685 as PWM driver, sensors, and a ROS based software architecture.
Finally, by following the methods presented in this thesis, it is conceivable to build an autonomous RACECAR that runs on ROS.
By following the means portrayed in this theory of work, it is conceivable to build up a self-governing vehicle
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