scholarly journals Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Mark Mario Morando ◽  
Qingyun Tian ◽  
Long T. Truong ◽  
Hai L. Vu
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Assessment Model ◽  
Traffic Crashes ◽  
Safety Measures ◽  
High Penetration ◽  
Simulation Based ◽  
Car Following Model ◽  
Surrogate Safety Measures ◽  
Signalised Intersection

Autonomous vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from the VISSIM traffic microsimulator using the Surrogate Safety Assessment Model (SSAM). Behaviours of human-driven vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car-following model. The safety investigation is conducted for two case studies, that is, a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at p<0.05). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at p<0.05).

scholarly journals Safety Evaluation for Connected and Autonomous Vehicles’ Exclusive Lanes considering Penetrate Ratios and Impact of Trucks Using Surrogate Safety Measures

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jian Zhang ◽  
Kunrun Wu ◽  
Min Cheng ◽  
Min Yang ◽  
Yang Cheng ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Safety Evaluation ◽  
Driver Model ◽  
Microscopic Simulation ◽  
Safety Measures ◽  
Traffic Efficiency ◽  
Safety Improvement ◽  
Demand Scenario ◽  
Surrogate Safety Measures

Plenty of studies on exclusive lanes for Connected and Autonomous Vehicle (CAV) have been conducted recently about traffic efficiency and safety. However, most of the previous research studies neglected comprehensive consideration of the safety impact on different market penetration rates (MPRs) of CAVs, traffic demands, and proportion of trucks in mixture CAVs with human’s driven vehicle environment. On this basis, this study is to (1) identify the safety impact on exclusive lanes for CAVs under different MPRs with different traffic demands and (2) investigate the safety impact of trucks for CAV exclusive lanes on mixture environment. Based on the Intelligent Driver Model (IDM), a CAV platooning control algorithm is proposed for modeling the driving behaviors of CAVs. A calibrated 7-kilometer freeway section microscopic simulation environment is built by VISSIM. Four surrogate safety measures, including both longitudinal and lateral safety risk indexes, are employed to evaluate the overall safety impacts of setting exclusive lanes. Main results indicate that (1) setting one exclusive lane is capable to improve overall safety environment in low demand, and two exclusive lanes are more suitable for high-demand scenario; (2) existence of trucks worsens overall longitudinal safety environment, and improper setting of exclusive lanes in high trucks, low MPR scenario has adverse effect on longitudinal safety; and (3) setting exclusive lanes have better longitudinal and lateral safety improvement in high-truck proportion scenarios. Setting one or two exclusive lanes led to [+42.4% to −52.90%] and [+45.7% to −55.2%] of longitudinal risks while [−1.8% to −87.1%] and [−2.1% to −85.3%] of lateral conflicts compared with the base scenario, respectively. Results of this study provide useful insight for the setting of exclusive lanes for CAVs in a mixture environment.

scholarly journals The City-Wide Impacts of the Interactions between Shared Autonomous Vehicle-Based Mobility Services and the Public Transportation System

2021 ◽  
Vol 13 (12) ◽  
pp. 6725
Author(s):  
Sehyun Tak ◽  
Soomin Woo ◽  
Sungjin Park ◽  
Sunghoon Kim
Keyword(s):  
Public Transportation ◽  
Autonomous Vehicles ◽  
Impact Analysis ◽  
Autonomous Vehicle ◽  
The Public ◽  
Private Vehicle ◽  
Simulation Based ◽  
Mode Share

When attempts are made to incorporate shared autonomous vehicles (SAVs) into urban mobility services, public transportation (PT) systems are affected by the changes in mode share. In light of that, a simulation-based method is presented herein for analyzing the manner in which mode choices of local travelers change between PT and SAVs. The data used in this study were the modal split ratios measured based on trip generation in the major cities of South Korea. Subsequently, using the simulated results, a city-wide impact analysis method is proposed that can reflect the differences between the two mode types with different travel behaviors. As the supply–demand ratio of SAVs increased in type 1 cities, which rely heavily on PT, use of SAVs gradually increased, whereas use of PT and private vehicles decreased. Private vehicle numbers significantly reduced only when SAVs and PT systems were complementary. In type 2 cities, which rely relatively less on PT, use of SAVs gradually increased, and use of private vehicles decreased; however, no significant impact on PT was observed. Private vehicle numbers were observed to reduce when SAVs were operated, and the reduction was a minimum of thrice that in type 1 cities when SAVs and PT systems interacted. Our results can therefore aid in the development of strategies for future SAV–PT operations.

Safety Impacts of Queue Warning in a Connected Vehicle Environment

2017 ◽  
Vol 2621 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Samaneh Khazraeian ◽  
Mohammed Hadi ◽  
Yan Xiao
Keyword(s):  
Traffic Safety ◽  
Assessment Model ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Warning Systems ◽  
Safety Measures ◽  
Dynamic Message Signs ◽  
Traffic Sensors ◽  
Message Signs ◽  
Surrogate Safety Measures

Queue warning systems (QWSs) have been implemented to increase traffic safety by informing drivers about queued traffic ahead so that they can react in a timely manner to the queue. Existing QWSs rely on fixed traffic sensors to detect the back of a queue. It is expected that if the transmitted messages from connected vehicles (CVs) are used for this purpose, detection can be faster and more accurate. In addition, with CVs, delivery of the messages can be done with onboard units instead of dynamic message signs and provide more flexibility on how far upstream of the queue the messages are delivered. This study investigates the accuracy and benefits of the QWS on the basis of CV data. The study evaluated the safety benefits of the QWS under different market penetrations of CVs in future years. Surrogate safety measures were estimated with simulation modeling combined with the surrogate safety assessment model tool. Results from this study indicate that a relatively low market penetration—about 3% to 6%—for the congested freeway examined in this study was sufficient for an accurate and reliable estimation of the queue length. Even at 3% market penetration, the CV-based estimation of back-of-queue identification was significantly more accurate than that based on detector measurements. The results also found that CV data allowed faster detection of the bottleneck and queue formation. Further, the QWS improved the safety conditions of the network by reducing the number of rear-end conflicts. Safety effects become significant when the compliance percentage with the queue warning messages is more than 15%.

scholarly journals Simulation-Based Performance Analysis for a Novel AVS/RS Technology with Movable Lifts

2021 ◽  
Vol 11 (5) ◽  
pp. 2283
Author(s):  
Boris Jerman ◽  
Banu Yetkin Ekren ◽  
Melis Küçükyaşar ◽  
Tone Lerher
Keyword(s):  
Autonomous Vehicles ◽  
Performance Metrics ◽  
Autonomous Vehicle ◽  
Arrival Rate ◽  
Point Location ◽  
Throughput Rate ◽  
Storage And Retrieval ◽  
Simulation Based ◽  
Initial Work ◽  
Novel Design

This paper studies a novel autonomous vehicle-based storage and retrieval system (AVS/RS) design with movable lifts (AVS/RS/ML). In the proposed system, there are aisle-captive lifts that are able to travel along the warehouse aisle to position themselves at the target column location. Those lifts can lift up/down the autonomous vehicles to/from the target storage compartment when they are in standstill. This novel design is proposed as an alternative to existing AVS/RSs to balance the resource utilizations as well as to provide an inexpensive solution with highly utilized autonomous vehicles (i.e., AGVs). As an initial work, for this novel system, two alternative operating designs under different racking configurations are experimented. We compare those two designs by their throughput rate performance metrics under the arrival rate scenarios with highly utilized AGVs (i.e., 95%). Besides, we experiment with two warehouse capacity scenarios: 900 and 1800 storage compartments. The results show that designs with two separate I/O point locations provide a better throughput rate than designs with single I/O point location. Besides, a decreased number of columns in the system improves the system’s performance.

scholarly journals A Review on Autonomous Driving Systems

2018 ◽  
Vol 5 (1) ◽  
pp. 1-16
Author(s):  
Salma Yaakub ◽  
Mohammed Hayyan Alsibai
Keyword(s):  
Autonomous Vehicles ◽  
Power Efficiency ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Planning System ◽  
Ground Vehicles ◽  
Traffic Crashes ◽  
Traffic System ◽  
Surrounding Environment ◽  
Analysis System

Autonomous vehicles are one of the promising solutions to reduce traffic crashes and improve mobility and traffic system. An autonomous vehicle is preferable because it helps in reducing the need for redesigning the infrastructure and because it improves the vehicle power efficiency in terms of cost and time taken to reach the destination. Autonomous vehicles can be divided into 3 types: Aerial vehicles, ground vehicles and underwater vehicles. General, four basic subsystems are integrated to enable a vehicle to move by itself which are: Position identifying and navigation system, surrounding environment situation analysis system, motion planning system and trajectory control system. In this paper, a review on autonomous vehicles and their related technological applications is presented to highlight the aspects of this industry as a part of industry 4.0 concept. Moreover, the paper discusses the best autonomous driving systems to be applied on our wheelchair project which aims at converting a manual wheelchair to a smart electric wheelchair

scholarly journals Evaluation of Roundabout Safety Performance through Surrogate Safety Measures from Microsimulation

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Orazio Giuffrè ◽  
Anna Granà ◽  
Maria Luisa Tumminello ◽  
Tullio Giuffrè ◽  
Salvatore Trubia ◽  
...  
Keyword(s):  
Prediction Model ◽  
Safety Assessment ◽  
Traffic Volume ◽  
Safety Performance ◽  
Assessment Model ◽  
Model Framework ◽  
Safety Measures ◽  
Crash Prediction ◽  
Crash Prediction Model ◽  
Surrogate Safety Measures

The paper presents a microsimulation-based approach for roundabout safety performance evaluation. Based on a sample of Slovenian roundabouts, the vehicle trajectories exported from AIMSUN and VISSIM were used to estimate traffic conflicts using the Surrogate Safety Assessment Model (SSAM). AIMSUN and VISSIM were calibrated for single-lane, double-lane and turbo roundabouts using the corresponding empirical capacity function which included critical and follow-up headways estimated through meta-analysis. Based on calibration of the microsimulation models, a crash prediction model from simulated peak hour conflicts for a sample of Slovenian roundabouts was developed. A generalized linear model framework was used to estimate the prediction model based on field collected crash data for 26 existing roundabouts across the country. Peak hour traffic distribution was simulated with AIMSUN, and peak hour conflicts were then estimated with the SSAM applying the filters identified by calibrating AIMSUN and VISSIM. The crash prediction model was based on the assumption that the crashes per year are a function of peak hour conflicts, the ratio of peak hour traffic volume to average daily traffic volume and the roundabout outer diameter. Goodness-of-fit criteria highlighted how well the model fitted the set of observations also better than the SSAM predictive model. The results highlighted that the safety assessment of any road unit may rely on surrogate safety measures, but it strongly depends on microscopic traffic simulation model used.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

2021 ◽  
pp. 095440702110090
Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Control Method ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Test Track ◽  
Optimal Control Method ◽  
The Future ◽  
On The Road

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.

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