146,677 research outputs found
Using acoustic sensor technologies to create a more terrain capable unmanned ground vehicle
Unmanned Ground Vehicle’s (UGV) have to cope with the most complex range of dynamic and variable obstacles and therefore need to be highly intelligent in order to cope with navigating in such a cluttered environment. When traversing over different terrains (whether it is a UGV or a commercial manned vehicle) different drive styles and configuration settings need to be selected in order to travel successfully over each terrain type. These settings are usually selected by a human operator in manned systems on what they assume the ground conditions to be, but how can an autonomous UGV ‘sense’ these changes in terrain or ground conditions? This paper will investigate noncontact acoustic sensor technologies and how they can be used to detect different terrain types by listening to the interaction between the wheel and the terrain. The results can then be used to create a terrain classification list for the system so in future missions it can use the sensor technology to identify the terrain type it is trying to traverse, which creating a more autonomous and terrain capable vehicle. The technology would also benefit commercial driver assistive technologie
GAS: Generating Fast and Accurate Surrogate Models for Autonomous Vehicle Systems
Modern autonomous vehicle systems use complex perception and control
components. These components can rapidly change during development of such
systems, requiring constant re-testing. Unfortunately, high-fidelity
simulations of these complex systems for evaluating vehicle safety are costly.
The complexity also hinders the creation of less computationally intensive
surrogate models.
We present GAS, the first approach for creating surrogate models of complete
(perception, control, and dynamics) autonomous vehicle systems containing
complex perception and/or control components. GAS's two-stage approach first
replaces complex perception components with a perception model. Then, GAS
constructs a polynomial surrogate model of the complete vehicle system using
Generalized Polynomial Chaos (GPC). We demonstrate the use of these surrogate
models in two applications. First, we estimate the probability that the vehicle
will enter an unsafe state over time. Second, we perform global sensitivity
analysis of the vehicle system with respect to its state in a previous time
step. GAS's approach also allows for reuse of the perception model when vehicle
control and dynamics characteristics are altered during vehicle development,
saving significant time.
We consider five scenarios concerning crop management vehicles that must not
crash into adjacent crops, self driving cars that must stay within their lane,
and unmanned aircraft that must avoid collision. Each of the systems in these
scenarios contain a complex perception or control component. Using GAS, we
generate surrogate models for these systems, and evaluate the generated models
in the applications described above. GAS's surrogate models provide an average
speedup of for safe state probability estimation (minimum
) and for sensitivity analysis (minimum ),
while still maintaining high accuracy
Object detection, distributed cloud computing and parallelization techniques for autonomous driving systems.
Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks
- …