1,068 research outputs found
Agile Autonomous Driving using End-to-End Deep Imitation Learning
We present an end-to-end imitation learning system for agile, off-road
autonomous driving using only low-cost sensors. By imitating a model predictive
controller equipped with advanced sensors, we train a deep neural network
control policy to map raw, high-dimensional observations to continuous steering
and throttle commands. Compared with recent approaches to similar tasks, our
method requires neither state estimation nor on-the-fly planning to navigate
the vehicle. Our approach relies on, and experimentally validates, recent
imitation learning theory. Empirically, we show that policies trained with
online imitation learning overcome well-known challenges related to covariate
shift and generalize better than policies trained with batch imitation
learning. Built on these insights, our autonomous driving system demonstrates
successful high-speed off-road driving, matching the state-of-the-art
performance.Comment: 13 pages, Robotics: Science and Systems (RSS) 201
Pemasaran Bagi Petualang Sebagai Kegiatan Komunikasi Pemasaran
Marketing for Adventurer aims adventurer as its consumers. Instead of revenue-based, adventurer consumer is based on lifestyle. Their hobbies are outdoor sports such as bungee jumping, rock climbing, off-road driving, wild-river canoeing, etc. Marketing for adventurer has specific strategic: (1) STP Strategy—market segmenting, market targeting, market positioning; (2) Customer Experience Strategy (CES) as the sequel of Experiential Marketing (EM). EM focuses more on emotion touch and feelings
GoonDAE: Denoising-Based Driver Assistance for Off-Road Teleoperation
Because of the limitations of autonomous driving technologies, teleoperation
is widely used in dangerous environments such as military operations. However,
the teleoperated driving performance depends considerably on the driver's skill
level. Moreover, unskilled drivers need extensive training time for
teleoperations in unusual and harsh environments. To address this problem, we
propose a novel denoising-based driver assistance method, namely GoonDAE, for
real-time teleoperated off-road driving. The unskilled driver control input is
assumed to be the same as the skilled driver control input but with noise. We
designed a skip-connected long short-term memory (LSTM)-based denoising
autoencoder (DAE) model to assist the unskilled driver control input by
denoising. The proposed GoonDAE was trained with skilled driver control input
and sensor data collected from our simulated off-road driving environment. To
evaluate GoonDAE, we conducted an experiment with unskilled drivers in the
simulated environment. The results revealed that the proposed system
considerably enhanced driving performance in terms of driving stability.Comment: This work has been submitted to the IEEE for possible publication.
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High impact activities in parks: best management practice and future research
Off-road driving, horseriding, rock climbing and similar activities can be lucrative for tour operators and important for local recreational groups, but contentious for management of national parks and protected areas, both because of safety and liability and because of potentially high environmental impacts. These include spreading weeds and pathogens, starting fires and crushing bird nests on beaches, amongst many others. In Australia as elsewhere, off-road vehicles and horses are allowed only in some places in some parks, and often only under permit. We have very little reliable scientific information to-date on just how serious these impacts may be, and on how well they might be managed through minimal-impact practices such as vehicle washdowns, stockfeed processing and seasonal closures. Such information can only be obtained through site specific ecological studies of the plants, animals and watercourses likely to be affected, differentiating tourism impacts from natural causes and fluctuations. This report examines management strategies for these activities worldwide and in Australia. Suggestions for best management practice and future research agendas are set
An Intelligent Predictive Algorithm for the Anti-Rollover Prevention of Heavy Vehicles for Off-Road Applications
Rollover detection and prevention are among the most critical aspects affecting the stability and safety assessment of heavy vehicles, especially for off-road driving applications. This topic has been studied in the past and analyzed in depth in terms of vehicle modelling and control algorithms design able to prevent the rollover risk. However, it still represents a serious problem for automotive carmakers due to the huge counts among the main causes for traffic accidents. The risk also becomes more challenging to predict for off-road heavy vehicles, for which the incipient rollover might be triggered by external factors, i.e., road irregularities, bank angles as well as by aggressive input from the driver. The recent advances in road profile measurement and estimation systems make road-preview-based algorithms a viable solution for the rollover detection. This paper describes a model-based formulation to analytically evaluate the load transfer dynamics and its variation due to the presence of road perturbations, i.e., road bank angle and irregularities. An algorithm to
detect and predict the rollover risk for heavy vehicles is also presented, even in presence of irregular road profiles, with the calculation of the ISO-LTR Predictive Time through the Phase-Plane analysis. Furthermore, the artificial intelligence techniques, based on the recurrent neural network approach, is also presented as a preliminary solution for a realistic implementation of the methodology. The paper finally assess the efficacy of the proposed rollover predictive algorithm by providing numerical results from the simulation of the most severe maneuvers in realistic off-road driving scenarios, also
demonstrating its promising predictive capabilities
Pay Attention to How You Drive: Safe and Adaptive Model-Based Reinforcement Learning for Off-Road Driving
Autonomous off-road driving is challenging as risky actions taken by the
robot may lead to catastrophic damage. As such, developing controllers in
simulation is often desirable as it provides a safer and more economical
alternative. However, accurately modeling robot dynamics is difficult due to
the complex robot dynamics and terrain interactions in unstructured
environments. Domain randomization addresses this problem by randomizing
simulation dynamics parameters, however this approach sacrifices performance
for robustness leading to policies that are sub-optimal for any target
dynamics. We introduce a novel model-based reinforcement learning approach that
aims to balance robustness with adaptability. Our approach trains a System
Identification Transformer (SIT) and an Adaptive Dynamics Model (ADM) under a
variety of simulated dynamics. The SIT uses attention mechanisms to distill
state-transition observations from the target system into a context vector,
which provides an abstraction for its target dynamics. Conditioned on this, the
ADM probabilistically models the system's dynamics. Online, we use a Risk-Aware
Model Predictive Path Integral controller (MPPI) to safely control the robot
under its current understanding of the dynamics. We demonstrate in simulation
as well as in multiple real-world environments that this approach enables safer
behaviors upon initialization and becomes less conservative (i.e. faster) as
its understanding of the target system dynamics improves with more
observations. In particular, our approach results in an approximately 41%
improvement in lap-time over the non-adaptive baseline while remaining safe
across different environments
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