scholarly journals Research on the Comfort of Vehicle Passengers Considering the Vehicle Motion State and Passenger Physiological Characteristics: Improving the Passenger Comfort of Autonomous Vehicles

2020 ◽  
Vol 17 (18) ◽  
pp. 6821
Author(s):  
Chang Wang ◽  
Xia Zhao ◽  
Rui Fu ◽  
Zhen Li
Keyword(s):  
Prediction Model ◽  
Autonomous Vehicles ◽  
Control Algorithm ◽  
Short Term Memory ◽  
Autonomous Vehicle ◽  
Physiological Characteristics ◽  
Binary Logistic Regression Analysis ◽  
Analysis Model ◽  
Passenger Comfort ◽  
Vehicle Motion

Comfort is a significant factor that affects passengers’ choice of autonomous vehicles. The comfort of an autonomous vehicle is largely determined by its control algorithm. Therefore, if the comfort of passengers can be predicted based on factors that affect comfort and the control algorithm can be adjusted, it can be beneficial to improve the comfort of autonomous vehicles. In view of this, in the present study, a human-driven experiment was carried out to simulate the typical driving state of a future autonomous vehicle. In the experiment, vehicle motion parameters and the comfort evaluation results of passengers with different physiological characteristics were collected. A single-factor analysis method and binary logistic regression analysis model were used to determine the factors that affect the evaluation results of passenger comfort. A passenger comfort prediction model was established based on the bidirectional long short-term memory network model. The results demonstrate that the accuracy of the passenger comfort prediction model reached 84%, which can provide a theoretical basis for the adjustment of the control algorithm and path trajectory of autonomous vehicles.

scholarly journals Autonomous Navigation of a Team of Unmanned Surface Vehicles for Intercepting Intruders on a Region Boundary

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 297
Author(s):  
Ali Marzoughi ◽  
Andrey V. Savkin
Keyword(s):  
Motion Control ◽  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Sufficient Conditions ◽  
Real Life ◽  
Unmanned Surface Vehicles ◽  
Necessary And Sufficient ◽  
See Region

We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders.

scholarly journals TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

2019 ◽  
Vol 33 ◽  
pp. 6120-6127 ◽  
Author(s):  
Yuexin Ma ◽  
Xinge Zhu ◽  
Sibo Zhang ◽  
Ruigang Yang ◽  
Wenping Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Autonomous Vehicle ◽  
Large City ◽  
Urban Traffic ◽  
Heterogeneous Traffic ◽  
Prediction Algorithm ◽  
Trajectory Prediction ◽  
Long Short Term Memory ◽  
Traffic Agents

To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods.

scholarly journals Accuracy–Power Controllable LiDAR Sensor System with 3D Object Recognition for Autonomous Vehicle

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5706
Author(s):  
Sanghoon Lee ◽  
Dongkyu Lee ◽  
Pyung Choi ◽  
Daejin Park
Keyword(s):  
Power Consumption ◽  
Autonomous Vehicles ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Sleep Mode ◽  
Cmos Process ◽  
Battery Power ◽  
Surrounding Environment ◽  
Transmission Period ◽  
Static Power

Light detection and ranging (LiDAR) sensors help autonomous vehicles detect the surrounding environment and the exact distance to an object’s position. Conventional LiDAR sensors require a certain amount of power consumption because they detect objects by transmitting lasers at a regular interval according to a horizontal angular resolution (HAR). However, because the LiDAR sensors, which continuously consume power inefficiently, have a fatal effect on autonomous and electric vehicles using battery power, power consumption efficiency needs to be improved. In this paper, we propose algorithms to improve the inefficient power consumption of conventional LiDAR sensors, and efficiently reduce power consumption in two ways: (a) controlling the HAR to vary the laser transmission period (TP) of a laser diode (LD) depending on the vehicle’s speed and (b) reducing the static power consumption using a sleep mode, depending on the surrounding environment. The proposed LiDAR sensor with the HAR control algorithm reduces the power consumption of the LD by 6.92% to 32.43% depending on the vehicle’s speed, compared to the maximum number of laser transmissions (Nx.max). The sleep mode with a surrounding environment-sensing algorithm reduces the power consumption by 61.09%. The algorithm of the proposed LiDAR sensor was tested on a commercial processor chip, and the integrated processor was designed as an IC using the Global Foundries 55 nm CMOS process.

SwiVR-Car-Seat

2021 ◽  
Vol 5 (4) ◽  
pp. 1-26
Author(s):  
Mark Colley ◽  
Pascal Jansen ◽  
Enrico Rukzio ◽  
Jan Gugenheimer
Keyword(s):  
Interface Design ◽  
Autonomous Vehicles ◽  
User Study ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
User Input ◽  
The Road ◽  
Car Seat ◽  
Perceived Realism ◽  
Vehicle Motion

Autonomous vehicles provide new input modalities to improve interaction with in-vehicle information systems. However, due to the road and driving conditions, the user input can be perturbed, resulting in reduced interaction quality. One challenge is assessing the vehicle motion effects on the interaction without an expensive high-fidelity simulator or a real vehicle. This work presents SwiVR-Car-Seat, a low-cost swivel seat to simulate vehicle motion using rotation. In an exploratory user study (N=18), participants sat in a virtual autonomous vehicle and performed interaction tasks using the input modalities touch, gesture, gaze, or speech. Results show that the simulation increased the perceived realism of vehicle motion in virtual reality and the feeling of presence. Task performance was not influenced uniformly across modalities; gesture and gaze were negatively affected while there was little impact on touch and speech. The findings can advise automotive user interface design to mitigate the adverse effects of vehicle motion on the interaction.

Design and implementation of human driving data–based active lane change control for autonomous vehicles

2020 ◽  
Vol 235 (1) ◽  
pp. 55-77
Author(s):  
Heungseok Chae ◽  
Yonghwan Jeong ◽  
Hojun Lee ◽  
Jongcherl Park ◽  
Kyongsu Yi
Keyword(s):  
Real World ◽  
Autonomous Vehicles ◽  
High Speed ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Lane Change ◽  
Driving Mode ◽  
Lane Changes ◽  
Safety Indices

This article describes the design, implementation, and evaluation of an active lane change control algorithm for autonomous vehicles with human factor considerations. Lane changes need to be performed considering both driver acceptance and safety with surrounding vehicles. Therefore, autonomous driving systems need to be designed based on an analysis of human driving behavior. In this article, manual driving characteristics are investigated using real-world driving test data. In lane change situations, interactions with surrounding vehicles were mainly investigated. And safety indices were developed with kinematic analysis. A safety indices–based lane change decision and control algorithm has been developed. In order to improve safety, stochastic predictions of both the ego vehicle and surrounding vehicles have been conducted with consideration of sensor noise and model uncertainties. The desired driving mode is decided to cope with all lane changes on highway. To obtain desired reference and constraints, motion planning for lane changes has been designed taking stochastic prediction-based safety indices into account. A stochastic model predictive control with constraints has been adopted to determine vehicle control inputs: the steering angle and the longitudinal acceleration. The proposed active lane change algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable lane changes in high-speed driving on highways have been demonstrated using our autonomous test vehicle.

Model to predict motion sickness within autonomous vehicles

2019 ◽  
Vol 234 (5) ◽  
pp. 1330-1345
Author(s):  
Spencer Salter ◽  
Cyriel Diels ◽  
Paul Herriotts ◽  
Stratis Kanarachos ◽  
Doug Thake
Keyword(s):  
Motion Sickness ◽  
Autonomous Vehicles ◽  
Systems Approach ◽  
Autonomous Vehicle ◽  
Human Motion ◽  
Independent Study ◽  
Vehicle Design ◽  
Specific Method ◽  
Motion Vision ◽  
Vehicle Motion

Background: Motion sickness is common within most forms of transport; it affects most of the population who experience varied symptoms at some stage in their lives. Thus far, there has been no specific method to quantify the predicted levels of motion sickness for a given vehicle design, task and route. Objective: To develop a motion sickness virtual prediction tool that includes the following inputs: human motion, vision, vehicle motion, occupant task and vehicle design. Method: A time domain analysis using a multi-body systems approach has been developed to provide the raw data for post-processing of vehicle motion, occupant motion and vision, based on a virtual route designed to provoke motion sickness, while the digital occupant undertakes a specific non-driving related task. Results: Predicted motion sickness levels are shared for a simple positional sweep of a vehicle cabin due to a prescribed motion and task. Two additional examples are shared within this study; first, it was found that the model can predict the difference found between sitting forwards and backwards in an autonomous vehicle. Second, analysis of a respected and independent study into auxiliary display height shows that the model can predict both relative and absolute levels between the two display heights congruent to the original physical experiment. Conclusion: It has been shown that the tool has been successful in predicting motion sickness in autonomous vehicles and is therefore of great use in guiding new future mobility solutions in the ability to tune vehicle dynamics and control alongside vision and design attributes.

scholarly journals Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles

2020 ◽  
Vol 10 (21) ◽  
pp. 7716
Author(s):  
Tamás Hegedűs ◽  
Balázs Németh ◽  
Péter Gáspár
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Low Complexity ◽  
The Real ◽  
Time Motion ◽  
Planning Algorithm ◽  
Vehicle Motion ◽  
Vehicle Trajectory

In the development of autonomous vehicles, the design of real-time motion-planning is a crucial problem. The computation of the vehicle trajectory requires the consideration of safety, dynamic and comfort aspects. Moreover, the prediction of the vehicle motion in the surroundings and the real-time planning of the autonomous vehicle trajectory can be complex tasks. The goal of this paper is to present low-complexity motion-planning for overtaking scenarios in parallel traffic. The developed method is based on the generation of a graph, which contains feasible vehicle trajectories. The reduction of the complexity in the real-time computation is achieved through the reduction of the graph with clustering. In the motion-planning algorithm, the predicted motion of the surrounding vehicles is taken into consideration. The prediction algorithm is based on density functions of the surrounding vehicle motion, which are developed through real measurements. The resulted motion-planning algorithm is able to guarantee a safe and comfortable trajectory for the autonomous vehicle. The effectiveness of the method is illustrated through simulation examples using a high-fidelity vehicle dynamic simulator.

scholarly journals Intersection Control and Delay Optimization for Autonomous Vehicles Flows Only as Well as Mixed Flows with Ordinary Vehicles

Vehicles ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 523-541
Author(s):  
Abdullah Baz ◽  
Ping Yi ◽  
Ahmad Qurashi
Keyword(s):  
Traffic Safety ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Signalized Intersection ◽  
Mixed Traffic ◽  
Intersection Safety ◽  
Vehicle To Infrastructure ◽  
Intersection Control

The rapidly improving autonomous vehicle (AV) technology will have a significant impact on traffic safety and efficiency. This study introduces a game-theory-based priority control algorithm for autonomous vehicles to improve intersection safety and efficiency with mixed traffic. By using vehicle-to-infrastructure (V2I) communications, this model allows an AV to exchange information with the roadside units (RSU) to support the decision making of whether an ordinary vehicle (OV) or an AV should pass the intersection first. The safety of vehicles is taken in different stages of decisions to assure collision-free intersection operations. Two different mathematical models have been developed, where model one is for an AV/AV situation and model two is when an AV meets an OV. A simulation model was developed to implement the algorithm and compare the performance of each model with the conventional traffic control at a four-legged signalized intersection and at a roundabout. Three levels of traffic volume and speed combinations were tested in the simulation. The results show significant reductions in delay for both cases; for case (I), AV/AV model, a 65% reduction compared to a roundabout and 84% compared to a four-legged signalized intersection, and for case (II), AV/OV model, the reduction is 30% and 89%, respectively.

scholarly journals Formulation and Theoretical Analysis of the Control Algorithm of the Autonomous Vehicle

2017 ◽  
Vol 9 (5) ◽  
pp. 571-577
Author(s):  
Paulius Skačkauskas ◽  
Vilius Mejeras ◽  
Edgar Sokolovskij
Keyword(s):  
Autonomous Vehicles ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Road User ◽  
Movement Trajectory ◽  
Optimization Task ◽  
Intellectual Control ◽  
Rational Operation ◽  
Effective Integration ◽  
Important System

With the technologies of the autonomous vehicles improving, one of the biggest tasks is safe and effective integration of the autonomous vehicles into the transport system. The safety of such vehicles depends on their ability to move stably and accurately in the desired trajectory. The intellectual control algorithm is the most important system, controlling the movement of the autonomous vehicle. That is why the formulation and analysis of the rational intellectual control algorithms based on different methods is a significant element when seeking that the autonomous vehicles become a valuable road user. A theoretical version of an intellectual control algorithm, which is designed to ensure an accurate following of the desired movement trajectory under different movement conditions, is presented and described in the work. The operation of the algorithm is based on the comparison of the mathematical models of two vehicles and the solution of the optimization task. The described control algorithm is presented in the environment of the software package MATLAB/Simulink, the analysis of the rational operation of the designed algorithm is done.

scholarly journals Learning-Based Stochastic Driving Model for Autonomous Vehicle Testing

2021 ◽  
pp. 036119812110357
Author(s):  
Lin Liu ◽  
Shuo Feng ◽  
Yiheng Feng ◽  
Xichan Zhu ◽  
Henry X. Liu
Keyword(s):  
Autonomous Vehicles ◽  
Large Scale ◽  
Short Term Memory ◽  
Autonomous Vehicle ◽  
Deterministic Models ◽  
Pilot Model ◽  
Proposed Model ◽  
Driving Model ◽  
Naturalistic Driving ◽  
Testing And Evaluation

In the simulation-based testing and evaluation of autonomous vehicles (AVs), how background vehicles (BVs) drive directly influences the AV’s driving behavior and further affects the test results. Most existing simulation platforms use either predetermined trajectories or deterministic driving models to model BV behaviors. However, predetermined BV trajectories cannot react to AV maneuvers, and deterministic models are different from real human drivers because of the lack of stochastic components and errors. Both methods lead to unrealistic traffic scenarios. This paper presents a learning-based stochastic driving model that meets the unique needs of AV testing (i.e., interactive and human-like stochasticity). The model is built based on the long short-term memory architecture. By incorporating the concept of quantile regression into the loss function of the model, the stochastic behaviors are reproduced without prior assumption of human drivers. The model is trained with the large-scale naturalistic driving data (NDD) from the Safety Pilot Model Deployment project and compared with a stochastic intelligent driving model (IDM). Analysis of individual trajectories shows that the proposed model can reproduce more similar trajectories of human drivers than IDM. To validate the ability of the proposed model in generating a naturalistic driving environment, traffic simulation experiments are implemented. The results show that traffic flow parameters such as speed, range, and time headway distribution match closely with the NDD, which is of significant importance for AV testing and evaluation.

Sign in / Sign up

Export Citation Format

Share Document