scholarly journals Too much pressure? Driving and restraining forces and pressures relating to the state of connected and autonomous vehicles in cities

2022 ◽  
Vol 13 ◽  
pp. 100507
Author(s):  
Ella Rebalski ◽  
Marco Adelfio ◽  
Frances Sprei ◽  
Daniel J.A. Johansson
Keyword(s):  
Autonomous Vehicles ◽  
The State

scholarly journals End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5443
Author(s):  
Hongyu Hu ◽  
Ziyang Lu ◽  
Qi Wang ◽  
Chengyuan Zheng
Keyword(s):  
Autonomous Vehicles ◽  
The State ◽  
Steering Wheel ◽  
Lane Change ◽  
Simulation Environment ◽  
Longitudinal Control ◽  
Driving Style ◽  
Lane Changing ◽  
Policy Gradient ◽  
End To End

Changing lanes while driving requires coordinating the lateral and longitudinal controls of a vehicle, considering its running state and the surrounding environment. Although the existing rule-based automated lane-changing method is simple, it is unsuitable for unpredictable scenarios encountered in practice. Therefore, using a deep deterministic policy gradient (DDPG) algorithm, we propose an end-to-end method for automated lane changing based on lidar data. The distance state information of the lane boundary and the surrounding vehicles obtained by the agent in a simulation environment is denoted as the state space for an automated lane-change problem based on reinforcement learning. The steering wheel angle and longitudinal acceleration are used as the action space, and both the state and action spaces are continuous. In terms of the reward function, avoiding collision and setting different expected lane-changing distances that represent different driving styles are considered for security, and the angular velocity of the steering wheel and jerk are considered for comfort. The minimum speed limit for lane changing and the control of the agent for a quick lane change are considered for efficiency. For a one-way two-lane road, a visual simulation environment scene is constructed using Pyglet. By comparing the lane-changing process tracks of two driving styles in a simplified traffic flow scene, we study the influence of driving style on the lane-changing process and lane-changing time. Through the training and adjustment of the combined lateral and longitudinal control of autonomous vehicles with different driving styles in complex traffic scenes, the vehicles could complete a series of driving tasks while considering driving-style differences. The experimental results show that autonomous vehicles can reflect the differences in the driving styles at the time of lane change at the same speed. Under the combined lateral and longitudinal control, the autonomous vehicles exhibit good robustness to different speeds and traffic density in different road sections. Thus, autonomous vehicles trained using the proposed method can learn an automated lane-changing policy while considering safety, comfort, and efficiency.

scholarly journals Model-based estimation of the state of vehicle automation as derived from the driver’s spontaneous visual strategies

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Damien Schnebelen ◽  
Camilo Charron ◽  
Franck Mars
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
The State ◽  
Partial Least Square ◽  
Least Square ◽  
Transition Matrices ◽  
Automated Driving ◽  
Gaze Behaviour ◽  
Motor Actions ◽  
Dynamic Indicators

When manually steering a car, the driver’s visual perception of the driving scene and his or her motor actions to control the vehicle are closely linked. Since motor behaviour is no longer required in an automated vehicle, the sampling of the visual scene is affected. Autonomous driving typically results in less gaze being directed towards the road centre and a broader exploration of the driving scene, compared to manual driving. To examine the corollary of this situation, this study estimated the state of automation (manual or automated) on the basis of gaze behaviour. To do so, models based on partial least square regressions were computed by considering the gaze behaviour in multiple ways, using static indicators (percentage of time spent gazing at 13 areas of interests), dynamic indicators (transition matrices between areas) or both together. Analysis of the quality of predictions for the different models showed that the best result was obtained by considering both static and dynamic indicators. However, gaze dynamics played the most important role in distinguishing between manual and automated driving. This study may be relevant to the issue of driver monitoring in autonomous vehicles.

scholarly journals Decision-Making System for Lane Change Using Deep Reinforcement Learning in Connected and Automated Driving

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 543 ◽  
Author(s):  
HongIl An ◽  
Jae-il Jung
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
The State ◽  
Lane Change ◽  
Vehicular Communication ◽  
Automated Driving ◽  
Lane Changing ◽  
State Information ◽  
Gradient Learning

Lane changing systems have consistently received attention in the fields of vehicular communication and autonomous vehicles. In this paper, we propose a lane change system that combines deep reinforcement learning and vehicular communication. A host vehicle, trying to change lanes, receives the state information of the host vehicle and a remote vehicle that are both equipped with vehicular communication devices. A deep deterministic policy gradient learning algorithm in the host vehicle determines the high-level action of the host vehicle from the state information. The proposed system learns straight-line driving and collision avoidance actions without vehicle dynamics knowledge. Finally, we consider the update period for the state information from the host and remote vehicles.

scholarly journals Investigating the state of connected and autonomous vehicles: a literature Review

2020 ◽  
Vol 48 ◽  
pp. 870-882
Author(s):  
Nirajan Shiwakoti ◽  
Peter Stasinopoulos ◽  
Francesco Fedele
Keyword(s):  
Literature Review ◽  
Autonomous Vehicles ◽  
The State

scholarly journals On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

2021 ◽  
Vol 11 (5) ◽  
pp. 2197
Author(s):  
Stefania Santini ◽  
Nicola Albarella ◽  
Vincenzo Maria Arricale ◽  
Renato Brancati ◽  
Aleksandr Sakhnevych
Keyword(s):  
Environmental Conditions ◽  
Autonomous Vehicles ◽  
Heavy Rain ◽  
Autonomous Driving ◽  
The State ◽  
Driver Assistance Systems ◽  
Model Based ◽  
Wide Range ◽  
Road Friction ◽  
Friction Estimation

In recent years, autonomous vehicles and advanced driver assistance systems have drawn a great deal of attention from both research and industry, because of their demonstrated benefit in reducing the rate of accidents or, at least, their severity. The main flaw of this system is related to the poor performances in adverse environmental conditions, due to the reduction of friction, which is mainly related to the state of the road. In this paper, a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The proposed technique is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach to evaluate the potential friction. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. The effectiveness of the proposed approach is disclosed via an extensive numerical analysis covering a wide range of environmental, traffic, and vehicle kinematic conditions. Results confirm the ability of the approach to properly automatically adapting the inter-vehicle space gap and to avoiding collisions also in adverse road conditions (e.g., ice, heavy rain).

scholarly journals Counter-Mapping the Spaces of Autonomous Driving

2018 ◽  
Author(s):  
Luis F. Alvarez León
Keyword(s):  
Private Sector ◽  
Autonomous Vehicles ◽  
Political Action ◽  
Economic Value ◽  
Autonomous Driving ◽  
The United States ◽  
The State ◽  
Production Of Space ◽  
Black Boxes ◽  
Self Driving Cars

In this article I provide an account of key tensions shaping the development of autonomous driving technologies, and explores how such tensions can open up avenues for counter-mapping the data spaces produced through these navigation technologies. The design and massive commercialization of autonomous vehicles implies the production of new models of space, generated through the integration of data collected through technologies such as lidar scanning, machine learning, and artificial intelligence. This production of space is bounded within the confines of the technological black boxes of the vehicles themselves, as well as the corporate black boxes of the companies that design and deploy them. However, there are key sources of tension surrounding the creation of these black boxes: those between market competitors; between the state and the private sector; and between civil society, the private sector, and the state. In this article I explore these tensions by focusing on the potential for counter-mapping as a means of critique, transparency, and political action across three separate aspects of the autonomous driving space-making process: (1) legislation, by examining the emergence of Right to Repair laws across the United States, beginning with the Automotive Right to Repair Law passed in Massachusetts in 2012; (2) design, through open source projects for building self-driving cars, exemplified by Udacity, a pioneer in this area; and (3) hacking, specifically interventions designed to open, critique, or disrupt autonomous driving technologies. These examinations are embedded in a political economic account that interrogates the ownership and control over the spaces produced through autonomous driving, as well as the economic value associated with such production of space. 

Practical Picture Processing

1974 ◽  
Vol 32 ◽  
pp. 338-339
Author(s):  
T. A. Welton
Keyword(s):  
Radiation Damage ◽  
Coherence Length ◽  
Spatial Information ◽  
State Of The Art ◽  
Coherent Radiation ◽  
The State ◽  
Energy Spread ◽  
Electron Micrograph ◽  
Picture Processing ◽  
Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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