11,120 research outputs found
Modeling and Verification of Naturalistic Lane Keeping System
In order to lower human drivers’ driving load and to enhance their systematic performance during driving, driver assistant systems have been introduced during the past few decades. Unfortunately, a large proportion of existing lane keeping techniques only focus on how to hold the car in the center of the lane, which may be contrary to the driver's natural motion sense. This research focuses on developing a rational and precise driver model with fully human driver operating behavior, which is crucial for the study of active safety technology and can provide drivers with a comfortable motion by imitating driving habits and trajectory.
Modeling a naturalistic lane keeping control requires understanding of how a driver operates the vehicle, analysis from vehicle lateral dynamics perspective, and knowledge of the combination of driver’s physical limitation. Another requirement to build an adaptive steering control model is to regard driver’s steering behavior as a reciprocal process between anticipation and compensation. Based on two angles (near and far angles) mechanism and experimental data recorded by the SIMULINK and dSpace co-platform, a close-loop system is designed. The whole system is a combination of a PI (proportional–integral) controller driver model and a vehicle model, which integrates vehicle lateral dynamic characteristics and upcoming road information. Moreover, a nonlinear steering driver model is designed. This open loop driver model can effectively correct steering wheel angle by minimizing the error between recorded driving data and that of the simulated model.
The simulation outcome shows that the proposed model captures human drivers’ behavior well and has an excellent adaptability towards the change of vehicle dynamic parameters and external disturbances
A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
Vehicle to Vehicle (V2V) communication has a great potential to improve
reaction accuracy of different driver assistance systems in critical driving
situations. Cooperative Adaptive Cruise Control (CACC), which is an automated
application, provides drivers with extra benefits such as traffic throughput
maximization and collision avoidance. CACC systems must be designed in a way
that are sufficiently robust against all special maneuvers such as cutting-into
the CACC platoons by interfering vehicles or hard braking by leading cars. To
address this problem, a Neural- Network (NN)-based cut-in detection and
trajectory prediction scheme is proposed in the first part of this paper. Next,
a probabilistic framework is developed in which the cut-in probability is
calculated based on the output of the mentioned cut-in prediction block.
Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed
which incorporates this cut-in probability to enhance its reaction against the
detected dangerous cut-in maneuver. The overall system is implemented and its
performance is evaluated using realistic driving scenarios from Safety Pilot
Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I
Modeling human learning involved in car driving
In this paper, car driving is considered at the level of human tracking and maneuvering in the context of other traffic. A model analysis revealed the most salient features determining driving performance and safety. Learning car driving is modelled based on a system theoretical approach and based on a neural network approach. The aim of this research is to assess the relative merit of both approaches to describe human learning behavior in car driving specifically and in operating dynamic systems in general
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions
We consider the paradigm of a black box AI system that makes life-critical
decisions. We propose an "arguing machines" framework that pairs the primary AI
system with a secondary one that is independently trained to perform the same
task. We show that disagreement between the two systems, without any knowledge
of underlying system design or operation, is sufficient to arbitrarily improve
the accuracy of the overall decision pipeline given human supervision over
disagreements. We demonstrate this system in two applications: (1) an
illustrative example of image classification and (2) on large-scale real-world
semi-autonomous driving data. For the first application, we apply this
framework to image classification achieving a reduction from 8.0% to 2.8% top-5
error on ImageNet. For the second application, we apply this framework to Tesla
Autopilot and demonstrate the ability to predict 90.4% of system disengagements
that were labeled by human annotators as challenging and needing human
supervision
Imitation Learning for Vision-based Lane Keeping Assistance
This paper aims to investigate direct imitation learning from human drivers
for the task of lane keeping assistance in highway and country roads using
grayscale images from a single front view camera. The employed method utilizes
convolutional neural networks (CNN) to act as a policy that is driving a
vehicle. The policy is successfully learned via imitation learning using
real-world data collected from human drivers and is evaluated in closed-loop
simulated environments, demonstrating good driving behaviour and a robustness
for domain changes. Evaluation is based on two proposed performance metrics
measuring how well the vehicle is positioned in a lane and the smoothness of
the driven trajectory.Comment: International Conference on Intelligent Transportation Systems (ITSC
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