scholarly journals A Hierarchical Autonomous Driver for a Racing Car: Real-Time Planning and Tracking of the Trajectory

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6008
Author(s):  
Margherita Montani ◽  
Leandro Ronchi ◽  
Renzo Capitani ◽  
Claudio Annicchiarico
Keyword(s):  
Real Time ◽  
Trajectory Planning ◽  
Vehicle Dynamics ◽  
Trajectory Optimization ◽  
Computational Cost ◽  
Path Tracking ◽  
Driver Model ◽  
Current Scenario ◽  
Optimum Model ◽  
Time Planning

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.

scholarly journals The Research of Car-Following Model Based on Real-Time Maximum Deceleration

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Longhai Yang ◽  
Xiqiao Zhang ◽  
Jiekun Gong ◽  
Juntao Liu
Keyword(s):  
Real Time ◽  
Vehicle Dynamics ◽  
Driver Model ◽  
Cruise Control ◽  
Maximum Acceleration ◽  
Car Following ◽  
Time Headway ◽  
Asphalt Road ◽  
Car Following Model ◽  
Maximum Deceleration

This paper is concerned with the effect of real-time maximum deceleration in car-following. The real-time maximum acceleration is estimated with vehicle dynamics. It is known that an intelligent driver model (IDM) can control adaptive cruise control (ACC) well. The disadvantages of IDM at high and constant speed are analyzed. A new car-following model which is applied to ACC is established accordingly to modify the desired minimum gap and structure of the IDM. We simulated the new car-following model and IDM under two different kinds of road conditions. In the first, the vehicles drive on a single road, taking dry asphalt road as the example in this paper. In the second, vehicles drive onto a different road, and this paper analyzed the situation in which vehicles drive from a dry asphalt road onto an icy road. From the simulation, we found that the new car-following model can not only ensure driving security and comfort but also control the steady driving of the vehicle with a smaller time headway than IDM.

Dynamics of Tire Blowout Events and Driver Assist System for Controlling Them

2015 ◽  
Author(s):  
Dan T. Horak ◽  
Shane K. Lack
Keyword(s):  
Real Time ◽  
Vehicle Dynamics ◽  
Driving Simulator ◽  
Driver Model ◽  
Dynamics Model ◽  
Vehicle Model ◽  
Human Driver ◽  
Driver Analysis ◽  
The One ◽  
Rear Tire

Dynamics of a pickup truck undergoing a rear tire blowout are analyzed as a system controlled by a human driver. Analysis is based on a large nonlinear vehicle dynamics model combined with a human driver model. The main reason why some tire blowouts result in accidents is identified. Insight is generated in experiments with human drivers in a driving simulator that runs the same vehicle model as the one used for analysis. A driver assist system for controlling tire blowouts is developed and validated in real time in the driving simulator.

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

Real-Time Onboard Trajectory Optimization Using Indirect Methods

2021 ◽  
Author(s):  
Sean M. Nolan ◽  
Clayton A. Smith ◽  
Jacob D. Wood
Keyword(s):  
Real Time ◽  
Trajectory Optimization ◽  
Indirect Methods

scholarly journals Trajectory planning in UAV emergency networks with potential underlaying D2D communication based on K-means

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Shuo Zhang ◽  
Shuo Shi ◽  
Tianming Feng ◽  
Xuemai Gu
Keyword(s):  
Trajectory Planning ◽  
Trajectory Optimization ◽  
Communication Systems ◽  
Complete Coverage ◽  
Wireless System ◽  
Fifth Generation ◽  
Emergency Communications ◽  
Trajectory Length ◽  
Post Disaster ◽  
Joint Evaluation

AbstractAt present, unmanned aerial vehicles (UAVs) have been widely used in communication systems, and the fifth-generation wireless system (5G) has further promoted the vigorous development of them. The trajectory planning of UAV is an important factor that affects the timeliness and completion of missions, especially in scenarios such as emergency communications and post-disaster rescue. In this paper, we consider an emergency communication network where a UAV aims to achieve complete coverage of potential underlaying device-to-device (D2D) users. Trajectory planning issues are grouped into clustering and supplementary phases for optimization. Aiming at trajectory length and sum throughput, two trajectory planning algorithms based on K-means are proposed, respectively. In addition, in order to balance sum throughput with trajectory length, we present a joint evaluation index. Then relying on this index, a third trajectory optimization algorithm is further proposed. Simulation results show the validity of the proposed algorithms which have advantages over the well-known benchmark scheme in terms of trajectory length and sum throughput.

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