1,331 research outputs found
Evolution of Neural Networks for Helicopter Control: Why Modularity Matters
The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so
Automotive sensor fusion systems for traffic aware adaptive cruise control
The autonomous driving (AD) industry is advancing at a rapid pace. New sensing technology for tracking vehicles, controlling vehicle behavior, and communicating with infrastructure are being added to commercial vehicles. These new automotive technologies reduce on road fatalities, improve ride quality, and improve vehicle fuel economy. This research explores two types of automotive sensor fusion systems: a novel radar/camera sensor fusion system using a long shortterm memory (LSTM) neural network (NN) to perform data fusion improving tracking capabilities in a simulated environment and a traditional radar/camera sensor fusion system that is deployed in Mississippi State’s entry in the EcoCAR Mobility Challenge (2019 Chevrolet Blazer) for an adaptive cruise control system (ACC) which functions in on-road applications. Along with vehicles, pedestrians, and cyclists, the sensor fusion system deployed in the 2019 Chevrolet Blazer uses vehicle-to-everything (V2X) communication to communicate with infrastructure such as traffic lights to optimize and autonomously control vehicle acceleration through a connected corrido
iDriving: Toward Safe and Efficient Infrastructure-directed Autonomous Driving
Autonomous driving will become pervasive in the coming decades. iDriving
improves the safety of autonomous driving at intersections and increases
efficiency by improving traffic throughput at intersections. In iDriving,
roadside infrastructure remotely drives an autonomous vehicle at an
intersection by offloading perception and planning from the vehicle to roadside
infrastructure. To achieve this, iDriving must be able to process voluminous
sensor data at full frame rate with a tail latency of less than 100 ms, without
sacrificing accuracy. We describe algorithms and optimizations that enable it
to achieve this goal using an accurate and lightweight perception component
that reasons on composite views derived from overlapping sensors, and a planner
that jointly plans trajectories for multiple vehicles. In our evaluations,
iDriving always ensures safe passage of vehicles, while autonomous driving can
only do so 27% of the time. iDriving also results in 5x lower wait times than
other approaches because it enables traffic-light free intersections
An Overview about Emerging Technologies of Autonomous Driving
Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007,
autonomous driving has been the most active field of AI applications. This
paper gives an overview about technical aspects of autonomous driving
technologies and open problems. We investigate the major fields of self-driving
systems, such as perception, mapping and localization, prediction, planning and
control, simulation, V2X and safety etc. Especially we elaborate on all these
issues in a framework of data closed loop, a popular platform to solve the long
tailed autonomous driving problems
Efficient Perception, Planning, and Control Algorithms for Vision-Based Automated Vehicles
Autonomous vehicles have limited computational resources; hence, their
control systems must be efficient. The cost and size of sensors have limited
the development of self-driving cars. To overcome these restrictions, this
study proposes an efficient framework for the operation of vision-based
automatic vehicles; the framework requires only a monocular camera and a few
inexpensive radars. The proposed algorithm comprises a multi-task UNet (MTUNet)
network for extracting image features and constrained iterative linear
quadratic regulator (CILQR) and vision predictive control (VPC) modules for
rapid motion planning and control. MTUNet is designed to simultaneously solve
lane line segmentation, the ego vehicle's heading angle regression, road type
classification, and traffic object detection tasks at approximately 40 FPS
(frames per second) for 228 x 228 pixel RGB input images. The CILQR controllers
then use the MTUNet outputs and radar data as inputs to produce driving
commands for lateral and longitudinal vehicle guidance within only 1 ms. In
particular, the VPC algorithm is included to reduce steering command latency to
below actuator latency to prevent self-driving vehicle performance degradation
during tight turns. The VPC algorithm uses road curvature data from MTUNet to
estimate the correction of the current steering angle at a look-ahead point to
adjust the turning amount. Including the VPC algorithm in a VPC-CILQR
controller on curvy roads leads to higher performance than CILQR alone. Our
experiments demonstrate that the proposed autonomous driving system, which does
not require high-definition maps, could be applied in current autonomous
vehicles.Comment: 10 figures, 13 page
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