scholarly journals Explore User Behaviour in Semi-autonomous Driving

2019 ◽  
Vol 1 (1) ◽  
pp. 3871-3880
Author(s):  
Yuan Shi ◽  
Jeyhoon Maskani ◽  
Giandomenico Caruso ◽  
Monica Bordegoni
Keyword(s):  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Lateral Position ◽  
Steering Wheel ◽  
Physiological Indicators ◽  
The Road ◽  
Road Map ◽  
Human Driver ◽  
And Performance ◽  
Physiological Sensors

AbstractThe control shifting between a human driver and a semi-autonomous vehicle is one of the most critical scenarios in the road-map of autonomous vehicle development. This paper proposes a methodology to study driver's behaviour in semi-autonomous driving with physiological-sensors-integrated driving simulators. A virtual scenario simulating take-over tasks has been implemented. The behavioural profile of the driver has been defined analysing key metrics collected by the simulator namely lateral position, steering wheel angle, throttle time, brake time, speed, and the take-over time. In addition, heart rate and skin conductance changes have been considered as physiological indicators to assess cognitive workload and reactivity. The methodology has been applied in an experimental study which results are crucial for taking insights on users’ behaviour. Results show that individual different driving styles and performance are able to be distinguished by calculating and elaborating the data collected by the system. This research provides potential directions for establishing a method to characterize a driver's behaviour in a semi-autonomous vehicle.

scholarly journals Multi-agent Approach to Predict the Trajectory of Road Infrastructure Agents Using a Convolutional Neural Network

2021 ◽  
Author(s):  
Andrey Azarchenkov ◽  
Maksim Lyubimov
Keyword(s):  
Neural Network ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Planning System ◽  
Object A ◽  
Additional Information ◽  
Road Map ◽  
Road Users ◽  
The Neural Network ◽  
Multi Agent

The problem of creating a fully autonomous vehicle is one of the most urgent in the field of artificial intelligence. Many companies claim to sell such cars in certain working conditions. The task of interacting with other road users is to detect them, determine their physical properties, and predict their future states. The result of this prediction is the trajectory of road users’ movement for a given period of time in the near future. Based on such trajectories, the planning system determines the behavior of an autonomous-driving vehicle. This paper demonstrates a multi-agent method for determining the trajectories of road users, by means of a road map of the surrounding area, working with the use of convolutional neural networks. In addition, the input of the neural network gets an agent state vector containing additional information about the object. A number of experiments are conducted for the selected neural architecture in order to attract its modifications to the prediction result. The results are estimated using metrics showing the spatial deviation of the predicted trajectory. The method is trained using the nuscenes test dataset obtained from lgsvl-simulator.

Combined Lane Keeping and Longitudinal Speed Control for Autonomous Driving

2019 ◽  
Author(s):  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Nicola Amati ◽  
Andrea Tonoli
Keyword(s):  
Control Method ◽  
Autonomous Vehicle ◽  
Speed Control ◽  
Autonomous Driving ◽  
External Input ◽  
The Road ◽  
Longitudinal Dynamic ◽  
Bicycle Model ◽  
Lane Keeping ◽  
Yaw Angle

Abstract This paper presents a technique for the lane keeping and the longitudinal speed control of an autonomous vehicle with the combination of an MPC and a PID control. The goal of the proposed control method is to minimize the lateral deviation and relative yaw angle with respect to the planned trajectory, while driving the vehicle at the highest acceptable longitudinal speed. The reference profile of the longitudinal speed is computed considering both the lateral and longitudinal dynamic of the vehicle. The vehicle is represented by means of a linear 3-DoF bicycle model. The control algorithm takes the road lane boundaries as the only external input. The proposed strategy is validated in simulation on three distinct driving scenarios.

scholarly journals An Indoor Vehicle-in-the-Loop Simulation Platform Testing Method for Autonomous Emergency Braking

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ying Gao ◽  
Xiangmo Zhao ◽  
Zhigang Xu ◽  
Jingjun Cheng ◽  
Wenwei Wang
Keyword(s):  
Traffic Safety ◽  
Autonomous Vehicle ◽  
Testing Time ◽  
Simulation Platform ◽  
The Road ◽  
Emergency Braking ◽  
Safety Function ◽  
Fast Solution ◽  
On The Road ◽  
And Performance

Autonomous vehicle (AV) is expected to be the ultimate solution for traffic safety, while autonomous emergency braking (AEB), as a crucial and fundamental active safety function of AV, has excellent potential for reducing fatalities and improving road safety. Although AV has the ability to cope with harsh conditions, it is supposed to be tested fully, systematically, and rigorously before it is officially commercialized. This study developed a novel indoor AV-in-the-loop (AVIL) simulation platform based on Client-Server (C/S) architecture for real full-scale AV testing. The proposed AVIL simulation platform consists of three parts: physical hardware components, software components, and various electrical interfaces that ensure the bidirectional virtual reality (VR) interaction. To validate the functionality and performance of the platform, this paper then adopted the Udwadia–Kalaba (U-K) approach to build the AEB system based on a typical driving situation due to the explicitness and simplicity of the U-K approach. Further, a group of real road-based experiments and AVIL-based experiments were conducted. The experimental results showed that the testing data obtained from the proposed AVIL platform have a high similarity to those of the real road tests, which means that the proposed AVIL platform is capable of simulating the AV running condition when it performs linear emergency braking on the road, thus confirming the feasibility and effectiveness of the AVIL platform for AV AEB testing. Simultaneously, the testing time and repeatability of the latter performed better. The findings of this study provide a new safe, effective, and fast solution to AV testing, and the practicability of this method has been verified.

scholarly journals Project ARES: Driverless Transportation System. Challenges and Approaches in an Unstructured Road

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1753
Author(s):  
Pablo Marin-Plaza ◽  
David Yagüe ◽  
Francisco Royo ◽  
Miguel Ángel de Miguel ◽  
Francisco Miguel Moreno ◽  
...  
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Vehicle Localization ◽  
Transport Efficiency ◽  
The Road ◽  
Operational Design ◽  
Low Coverage

The expansion of electric vehicles in urban areas has paved the way toward the era of autonomous vehicles, improving the performance in smart cities and upgrading related driving problems. This field of research opens immediate applications in the tourism areas, airports or business centres by greatly improving transport efficiency and reducing repetitive human tasks. This project shows the problems derived from autonomous driving such as vehicle localization, low coverage of 4G/5G and GPS, detection of the road and navigable zones including intersections, detection of static and dynamic obstacles, longitudinal and lateral control and cybersecurity aspects. The approaches proposed in this article are sufficient to solve the operational design of the problems related to autonomous vehicle application in the special locations such as rough environment, high slopes and unstructured terrain without traffic rules.

scholarly journals Autonomous Drifting Using Reinforcement Learning

2021 ◽  
Author(s):  
László Orgován ◽  
Tamás Bécsi ◽  
Szilárd Aradi
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
The Road ◽  
Model Free ◽  
On The Road ◽  
Self Driving Cars ◽  
Planning Problems

Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA.

scholarly journals To the Problem of Increased Efficiency in Health Care Strategic Management

2019 ◽  
pp. 50-55 ◽  
Author(s):  
Sergey Dvoynikov ◽  
Svetlana Arhipova
Keyword(s):  
Health Care ◽  
Strategic Management ◽  
Balanced Scorecard ◽  
Health Facilities ◽  
The Balanced Scorecard ◽  
The Road ◽  
Road Map ◽  
Modern Health Care ◽  
And Performance ◽  
Key Indicators

Introduction. Modern health care is a leader in the social sector in terms of the number of public-private partnership projects. Monitoring of the implementation of strategic projects proves that the targets on the key indicators of the road map are reached. However, experts give a negative assessment of the availability and quality of medical care. Methods. The article describes the experience of using the balanced scorecard as an instrument for linking the key health care indicators and performance indicators of individual health facilities. Principles underlying cohesion and balance of the separate elements of the system are justified. Methods for identifying a statistically significant causal relationship between indicators are analyzed. A general model for the assessment of the attainment of the strategic goals by taking into account the three-level healthcare system is proposed. Results. The presented details on the structure of indicators could be used by managers of health facilities for the development of their own strategies. The article concluded that further specification on the methodology of obtaining objective information to assess the success in health care strategic management is necessary.

scholarly journals On infusing reachability-based safety assurance within planning frameworks for human–robot vehicle interactions

2020 ◽  
Vol 39 (10-11) ◽  
pp. 1326-1345 ◽  
Author(s):  
Karen Leung ◽  
Edward Schmerling ◽  
Mengxuan Zhang ◽  
Mo Chen ◽  
John Talbot ◽  
...  
Keyword(s):  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
The Other ◽  
Proactive Behavior ◽  
The Road ◽  
Planning Algorithm ◽  
Intent Prediction ◽  
On The Road ◽  
Free Interaction

Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is ensuring safety on the road: a key challenge in doing so is accounting for uncertainty in human driver actions without unduly impacting planner performance. This article introduces a minimally interventional safety controller operating within an autonomous vehicle control stack with the role of ensuring collision-free interaction with an externally controlled (e.g., human-driven) counterpart while respecting static obstacles such as a road boundary wall. We leverage reachability analysis to construct a real-time (100 Hz) controller that serves the dual role of (i) tracking an input trajectory from a higher-level planning algorithm using model predictive control, and (ii) assuring safety by maintaining the availability of a collision-free escape maneuver as a persistent constraint regardless of whatever future actions the other car takes. A full-scale steer-by-wire platform is used to conduct traffic-weaving experiments wherein two cars, initially side-by-side, must swap lanes in a limited amount of time and distance, emulating cars merging onto/off of a highway. We demonstrate that, with our control stack, the autonomous vehicle is able to avoid collision even when the other car defies the planner’s expectations and takes dangerous actions, either carelessly or with the intent to collide, and otherwise deviates minimally from the planned trajectory to the extent required to maintain safety.

scholarly journals Construction and Verification of a High-Precision Base Map for an Autonomous Vehicle Monitoring System

2019 ◽  
Vol 8 (11) ◽  
pp. 501
Author(s):  
Sungil Ham ◽  
Junhyuck Im ◽  
Minjun Kim ◽  
Kuk Cho
Keyword(s):  
Control System ◽  
High Precision ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Automobile Engineering ◽  
The Road ◽  
Vehicle Monitoring ◽  
Map Design ◽  
Operation Status

For autonomous driving, a control system that supports precise road maps is required to monitor the operation status of autonomous vehicles in the research stage. Such a system is also required for research related to automobile engineering, sensors, and artificial intelligence. The design of Google Maps and other map services is limited to the provision of map support at 20 levels of high-resolution precision. An ideal map should include information on roads, autonomous vehicles, and Internet of Things (IOT) facilities that support autonomous driving. The aim of this study was to design a map suitable for the control of autonomous vehicles in Gyeonggi Province in Korea. This work was part of the project “Building a Testbed for Pilot Operations of Autonomous Vehicles”. The map design scheme was redesigned for an autonomous vehicle control system based on the “Easy Map” developed by the National Geography Center, which provides free design schema. In addition, a vector-based precision map, including roads, sidewalks, and road markings, was produced to provide content suitable for 20 levels. A hybrid map that combines the vector layer of the road and an unmanned aerial vehicle (UAV) orthographic map was designed to facilitate vehicle identification. A control system that can display vehicle and sensor information based on the designed map was developed, and an environment to monitor the operation of autonomous vehicles was established. Finally, the high-precision map was verified through an accuracy test and driving data from autonomous vehicles.

scholarly journals DESIGN PROCEDURE AND OPTIMIZATION OF STEERING SYSTEM FOR FORMULA STUDENT CAR.

2021 ◽  
Vol 10 (2) ◽  
pp. 82-102
Keyword(s):  
Design Procedure ◽  
Steering System ◽  
Design Parameters ◽  
Steering Wheel ◽  
Formula Sae ◽  
The Road ◽  
Manufacturing Assembly ◽  
And Performance ◽  
Steering Effort ◽  
Clear Idea

Steering system is one of the major part of any Automotive design. It is responsible for transferring drivers input to the wheels and gives feedback from road to driver. To design a proper steering system for a formula SAE vehicle, several design parameters needed to be determined. The most important design intent for this system was to provide the driver with a system that did not require excessive steering input force or excessive steering wheel rotation, proper wheel feedback, and adequate wheel steering angle to allow the driver to navigate the tightest corners on the autocross course. This report gives clear idea about how the steering geometry has to be decided for formula student car by using CAD Software Solidworks, and how to select from different possible alternatives. Also, designing of steering components like rack and pinion, shafts and Steering wheel is explained in the report. The overall report can be divided into two objectives. The first is to design the proper steering geometry for the car. The second objective is to design the steering system components, so that the steering effort decreases without compensating the feedback which is obtained from the road. Designing and optimizing of steering system and its components is done considering the rules provided by Formula Bharat 2020, ergonomics, drivers safety, Components Manufacturing, Assembly and performance. The CAD file is entirely developed in Solidworks 2018-19. Static force analysis on components is also performed in Solidworks 2018-19.

scholarly journals PATH TRACKING FOR AN AUTONOMOUS VEHICLE DURING EMERGENCY CONDITION

2021 ◽  
Vol 2 (2) ◽  
pp. 025-033
Author(s):  
Zulkarnain Zulkarnain ◽  
Ismail Thamrin ◽  
Firmansyah Burlian ◽  
Indah Novianty
Keyword(s):  
Trajectory Tracking ◽  
Autonomous Vehicle ◽  
Orientation Angle ◽  
Lateral Position ◽  
Path Tracking ◽  
Control Technique ◽  
Vehicle Speed ◽  
The Road ◽  
The Difference ◽  
Vehicle Path

An autonomous vehicle's primary function is detecting and tracking the road course precisely and correctly without a driver's assistance. As a result, implementing appropriate controllers is critical for improving the vehicle's stability and movement responsiveness. The performance of adaptive Stanley controlled is evaluated in this paper using numerical simulations. The Stanley controller's most common geometric controller for vehicle path tracking algorithms is compared based on their trajectory tracking analyses on various vehicle speed maneuvers. Stanley calculates steering based on the difference between the vehicle's lateral position and heading angle. The difference between desired coordinates and present coordinates of the vehicle along the path is used to calculate lateral, longitudinal, and vehicle heading orientation angle using the future prediction control technique. The results demonstrate that the Stanley controller outperforms the emergency trajectory with more consistent trajectory tracking and steady-state error.

Sign in / Sign up

Export Citation Format

Share Document