# The Impact of In-Vehicle Traffic Lights on Driving Characteristics in the Presence of Obstructed Line-of-Sight

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. The Impact of Road Conditions on Driving Characteristics

#### 1.2. The Impact of ADAS on Driving Characteristics

#### 1.3. The Impact of Road Conditions and ADAS on Driving Characteristics

#### 1.4. The Impact of IVTLs on Driving Characteristics

## 2. Methods

#### 2.1. Following Distance Model with Partial Deployment of IVTLs

#### 2.1.1. Only the Preceding Vehicle Equipped with IVTLs

#### Green Light of ORTL

- Both vehicles traverse the intersection

_{1}, is less than the maximum distance, denoted as x

_{G}, that the vehicle can travel within the remaining green time, denoted as t

_{G}, the vehicle will be able to traverse the intersection. As depicted in Figure 4a, the preceding vehicle initiates acceleration with a′

_{11}and reaches the road speed limit v

_{RSL}within time t

_{11}. The vehicle then maintains this speed for the duration of time t

_{12}. Then, as the vehicle approaches the intersection, it reduces speed to the intersection speed limit v

_{ISL}over a period of time t

_{13}, until traversing the intersection. The maximum distance x

_{G}

_{1}that the vehicle can travel within the remaining green time t

_{G}can be calculated using Equation (1). The following vehicle initially maintains its initial speed v

_{2}for a duration of time t

_{21}, subsequently increasing its speed to v′

_{2}(which is less than v

_{RSL}) over time t

_{22}. The trajectory of this vehicle is similar to that of the preceding vehicle, as described by Equation (2), whereby the maximum distance x

_{G}

_{2}that the vehicle can travel within the remaining green time t

_{G}can be calculated. The distance covered by the following vehicle over time can be computed using Equation (3).

- Only the preceding vehicle traverses the intersection

_{2}, is greater than the maximum distance, denoted as x

_{G}

_{2}, that the vehicle can travel within the remaining green time, denoted as t

_{G}, which can be calculated using Equation (4).

- Neither the preceding vehicle nor the following vehicle traverses the intersection

_{G}

_{1}and x

_{G}

_{2}, that each vehicle can travel within the remaining green time t

_{G}can be calculated using Equations (6) and (7), respectively.

#### Red Light of ORTL

- Both vehicles traverse the intersection

_{1}, is less than the maximum distance, denoted as x

_{R}, that the vehicle can travel within the remaining time, denoted as t

_{R}, the vehicle will be unable to traverse the intersection. The maximum distances, x

_{R}

_{1}and x

_{R}

_{2}, that the preceding and following vehicles can travel within the remaining time t

_{R}, respectively, can be determined by applying Equations (9) and (10). As illustrated in Figure 5a, the preceding vehicle, by decelerating at a rate of a

_{11}, reduces its velocity to v′

_{1}over the course of time t

_{11}, while the following vehicle maintains its initial velocity for a duration of time t

_{21}. Subsequently, the preceding vehicle, by accelerating at a rate of a′

_{11}, increases its velocity to v″

_{1}over time t

_{12}. Furthermore, the following vehicle, by first decelerating at a rate of a

_{21}and then accelerating at a rate of a′

_{21}, over time t

_{22}and t

_{23}, respectively, adjusts its velocity. Finally, both the preceding and following vehicles reduce their velocities below the intersection speed limit to traverse the intersection.

- Neither the preceding vehicle nor following vehicle traverses the intersection

_{R}

_{1}and x

_{R}

_{2}, that the preceding and following vehicles can travel within the remaining time t

_{R}can be determined by applying Equations (12) and (13), respectively. Furthermore, the distance-over-time of the following vehicle, D′, can also be obtained by utilizing Equation (11).

#### 2.1.2. Only the Following Vehicle Equipped with IVTLs

#### Green Light of ORTLs

- Both vehicles traverse the intersection

_{11}is maintained and then decreased to the intersection speed limit in time t

_{12}, and the velocity is maintained in time t

_{13}. However, the following vehicle with acceleration a′

_{21}increases the velocity to v′

_{2}(below v

_{RSL}) in time t

_{21}, and then with deceleration a

_{21}decreases to the intersection speed limit using time t

_{22}. Finally, the vehicle retains the velocity in time t

_{23}. In addition, x

_{G}

_{1}and x

_{G}

_{2}can be calculated by Equations (14) and (15), respectively.

- Neither the preceding vehicle nor following vehicle traverses the intersection

_{G}

_{1}and x

_{G}

_{2}can easily be obtained using Equations (17) and (18), respectively.

#### Red Light of the ORTL

- Both vehicles traverse the intersection

_{1}, exceeds the maximum distance that the vehicle can cover in the remaining time, t

_{R}, represented as x

_{R}

_{1}. Analogously, the same condition applies to the subsequent vehicle. The values of x

_{R}

_{1}and x

_{R}

_{2}can be computed through the application of Equations (20) and (21), respectively. As depicted in Figure 9a, during the latter stages of the journey, the preceding vehicle reduces its speed as opposed to coming to a complete halt at the stop line. This is achieved through a combination of deceleration, denoted as a

_{21}, which reduces the velocity to v′

_{2}in time t

_{21}, followed by acceleration, denoted as a′

_{21}, which increases the velocity to v″

_{2}(below v

_{RSL}) in time t

_{22}, and finally deceleration, denoted as a

_{22}, which reduces the velocity to v

`‴`

_{2}in time t

_{23}.

- Neither the preceding vehicle nor following vehicle traverses the intersection

_{R}, denoted as x

_{R}

_{1}, and the same for the following vehicle, denoted as x

_{R}

_{2}, can be computed through the application of Equations (23) and (24), respectively. Furthermore, the temporal distance metric, D′, can also be obtained through the utilization of Equation (22).

#### 2.1.3. IVTL Mechanism of Both Vehicles

#### 2.2. Evaluation Indicators

_{preceding}denotes the travel time of the preceding vehicle, and T

_{following}denotes the travel time of the subsequent vehicle. The metric T

_{average}signifies the average travel time of both the preceding and following vehicles.

_{following}denotes the velocity of the subsequent vehicle, V

_{preceding}denotes the velocity of the preceding vehicle, and D represents the following distance.

_{t}represents the absolute value of the steering angle at a given instant, S

_{t}

_{+nT}represents the absolute value of the steering angle at the subsequent instant, and T signifies the sampling period.

_{t}represents the absolute value of the brake pressure at a given instant, and P

_{t}

_{+nT}represents the absolute value of the brake pressure at the subsequent instant.

#### 2.3. Simulated Scenarios with Obstructed ORTLs

#### 2.3.1. “Vehicle” Configuration of Simulation Software

#### 2.3.2. “Road” Configuration of Simulation Software

## 3. Experiment

#### 3.1. Participants

#### 3.2. Experimental Apparatus

#### 3.3. Experiment Process

## 4. Analysis

_{average}, maxS and maxP were extracted. Finally, the evaluation indicators were classified according to the scenarios and different deployments of IVTLs. The statistical significance threshold was set at 0.05. External environmental factors (sunny or foggy weather or ORTLs obstructed by a large vehicle) and internal conditions (incomplete or complete deployment of IVTLs) were used as variables for one-way analysis of variance.

#### 4.1. Analysis of Driving Safety

#### 4.2. Analysis of Driving Maneuverability

_{average}. The numerical values marked with a red “+” sign are considered outliers for this data. Due to their extremely high or low values, they may have an impact on the overall analysis. Therefore, they have been removed from the dataset.

_{average}decreased by about 30%, indicating that deploying IVTLs can effectively improve driving maneuverability in this scenario.

#### 4.3. Analysis of Driving Comfort

## 5. Discussion

## 6. Conclusions

_{average}was approximately 30% lower in comparison to the same scenario without IVTLs, indicating an improvement in vehicle mobility. In scenarios where the ORTLs were blocked by a truck, the minTTC was approximately 50% lower in comparison to the same scenario without IVTLs, thus enhancing vehicle safety. In conclusion, the IVTLs can effectively aid drivers in traversing intersections, even under conditions of line-of-sight obstruction to ORTLs and incomplete deployment of IVTLs.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Lu, H.; Chen, M.; Kuang, W. The Impacts of Abnormal Weather and Natural Disasters on Transport and Strategies for Enhancing Ability for Disaster Prevention and Mitigation. Transp. Policy
**2020**, 98, 2–9. [Google Scholar] [CrossRef] - Xu, M.; Wang, H.; Chu, S. Traffic Simulation and Visual Verification in Smog. ACM Trans. Intell. Syst. Technol.
**2018**, 10, 1–17. [Google Scholar] [CrossRef] - Reinolsmann, N.; Alhajyaseen, W.; Brijs, T. Sandstorm Animations on Rural Expressways: The Impact of Variable Message Sign Strategies on Driver Behavior in Low Visibility Conditions. Transp. Res. Part F Traffic Psychol. Behav.
**2021**, 78, 308–325. [Google Scholar] [CrossRef] - Sun, Y.; Xu, J. Research on Drivers’ Behavior Characteristics of Expressway Straight Section under Moderate Rainfall. IOP Conf. Ser. Earth Environ. Sci.
**2021**, 634, 012134. [Google Scholar] [CrossRef] - Bobermin, M.P.; Silva, M.M.; Ferreira, S. Driving Simulators to Evaluate Road Geometric Design Effects on Driver Behaviour: A Systematic Review. Accid. Anal. Prev.
**2021**, 150, 105923. [Google Scholar] [CrossRef] - Mohammed, D.; Aldoski, Z.; Kattan, R. Geometrical Design Errors in Duhok Intersections by Driver Behavior. J. Univ. Babylon Eng. Sci.
**2018**, 26, 395–410. [Google Scholar] - Yan, Y.; Dai, Y.; Li, X.; Tang, J.; Guo, Z. Driving Risk Assessment Using Driving Behavior Data under Continuous Tunnel Environment. Traffic Inj. Prev.
**2019**, 20, 807–812. [Google Scholar] [CrossRef] - Zhang, G.; Chen, J.; Zhao, J. Safety Performance Evaluation of a Three-Leg Unsignalized Intersection Using Traffic Conflict Analysis. Math. Probl. Eng.
**2017**, 2017, 2948750. [Google Scholar] [CrossRef] - Manchon, J.; Bueno, M.; Navarro, J. How the Initial Level of Trust in Automated Driving Impacts Drivers’ Behaviour and Early Trust Construction. Transp. Res. Part F Traffic Psychol. Behav.
**2022**, 86, 281–295. [Google Scholar] [CrossRef] - Toledo, G.; Shiftan, Y. Can Feedback from In-Vehicle Data Records Improve Driver Behavior and Reduce Fuel Comsumption? Transp. Res. Part A Policy Pract.
**2016**, 94, 194–204. [Google Scholar] [CrossRef] - Yang, Y.; Yan, J.; Guo, J.; Kuang, Y.J.; Yin, M.Y.; Wang, S.N.; Ma, C.Y. Driving Behavior Analysis of City Buses based on Real-Time GNSS Traces and Road Information. Sensors
**2021**, 21, 687. [Google Scholar] [CrossRef] - Kim, B.; Baek, Y. Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis. Sensors
**2020**, 20, 5197. [Google Scholar] [CrossRef] - Noble, A.M.; Dingus, T.A.; Doerzaph, Z.R. Influence of In-Vehicle Adaptive Stop Display on Driving Behavior and Safety. IEEE Trans. Intell. Transp. Syst.
**2016**, 17, 2767–2776. [Google Scholar] [CrossRef] - Bao, S.; Wu, L.; Yu, B.; Sayer, J.R. An Examination of Teen drivers’ Car-Following Behavior under Naturalistic Driving Conditions: With and without an Advanced Driving Assistance System. Accid. Anal. Prev.
**2020**, 147, 105762. [Google Scholar] [CrossRef] - Gouribhatla, R.; Pulugurtha, S.S. Drivers’ Behavior when Driving Vehicles with or without Advanced Driver Assistance Systems: A Driver Simulator based Study. Transp. Res. Interd. Persp.
**2022**, 13, 100545. [Google Scholar] [CrossRef] - Voinea, G.D.; Postelnicu, C.C.; Duguleana, M.; Mogan, G.L.; Socianu, R. Driving Performance and Technology Acceptance Evaluation in Real Traffic of a Smartphone-Based Driver Assistance System. Int. J. Environ. Res. Public Health
**2020**, 17, 7098. [Google Scholar] [CrossRef] - Liu, R.; Yan, X.D.; Ma, S.W.; Xue, Q.W. Eye Movement as a Function to Explore the Effects of Improved Signs Design and Audio Warning on Drivers’ Behavior at STOP-Sign-Controlled Grade Crossings. Accid. Anal. Prev.
**2022**, 172, 106693. [Google Scholar] [CrossRef] - Orlovska, J.; Novakazi, F.; Lars-Ola, B.; Karlsson, M.; Wickman, C.; Soderberg, R. Effects of the Driving Context on the Usage of Automated Driver Assistence Systems (ADAS)-Naturalistic Driving Study for ADAS Evaluation. Transp. Res. Interd. Persp.
**2020**, 4, 100093. [Google Scholar] - Petraki, V.; Ziakopoulos, A.; Yannis, G. Combined Impact of Road and Traffic Characteristic on Driver Behavior Using Smartphone Sensor Data. Accid. Anal. Prev.
**2020**, 144, 105657. [Google Scholar] [CrossRef] - Pappalardo, G.; Caponetto, R.; Varrica, R.; Cafiso, S. Assessing the Operational Design Domain of Lane Support System for Automated Vehicles in Different Weather and Road Conditions. J. Traffic Transp. Eng.
**2022**, 9, 631–644. [Google Scholar] [CrossRef] - Pappalardo, G.; Cafiso, S.; Graziano, A.D.; Severino, A. Decision Tree Method to Analyze the Performance of Lane Support Systems. Sustainability
**2021**, 13, 846. [Google Scholar] [CrossRef] - Zolali, M.; Mirbaha, B.; Layegh, M.; Behnood, H.R. A Behavioral Model of Drivers’ Mean Speed Influenced by Weather Conditions, Road Geometry, and Driver Characteristics Using a Driving Simulator Study. Adv. Civ. Eng.
**2021**, 2021, 5542905. [Google Scholar] [CrossRef] - Yao, Y.; Zhao, X.H.; Zhang, Y.L.; Chen, C.; Rong, J. Modeling of Individual Vehicle Safety and Fuel Consumption under Comprehensive External conditions. Transp. Res. Part D Transp. Environ.
**2020**, 79, 102224. [Google Scholar] [CrossRef] - Tonguz, O.K. Biologically Inspired Solutions to Fundamental Transportation Problem. IEEE Commun. Mag.
**2011**, 11, 106–115. [Google Scholar] [CrossRef] - Yang, B.; Zheng, R.; Shimono, K.; Kaizuka, T.; Nakano, K. Evaluation of the Effects of In-Vehicle Traffic Light on Driving Performances for Unsignalised Intersections. IET Intell. Transp. Syst.
**2017**, 11, 76–83. [Google Scholar] [CrossRef] - Yang, B.; Zheng, R.; Kaizuka, T.; Nakano, K. Influences of Waiting Time on Driver Behaviors While Implementing In-Vehicle Traffic Light for Priority-Controlled Unsignalized Intersections. J. Adv. Transp.
**2017**, 2017, 7871561. [Google Scholar] [CrossRef] - Yang, B.; Zheng, R.; Kaizuka, T. Analysis of Driver Behaviors while Using In-Vehicle Traffic Light with Partial Deployment of V2I Communication. In Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China, 26–30 June 2018; pp. 19–24. [Google Scholar]
- Yang, B.; Kaizuka, T.; Nakano, K. Drivers’ Trust Model while Using In-Vehicle Traffic Lights in a Partial Deployment Scenario. In Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27–30 October 2019; pp. 1588–1593. [Google Scholar]
- Ferreira, M.; Fernandes, M.; Conceição, H. Self-Organized Traffic Control. In Proceedings of the Seventh ACM International Workshop on Vehicular Internetworking, Chicago, IL, USA, 24 September 2010; Association for Computing Machinery: New York, NY, USA, 2010; pp. 85–90. [Google Scholar]
- Ferrreira, M.; Ore, P.M. On the Impact of Virtual Traffic Lights on Carbon Emissions Mitigation. IEEE Trans. Intell. Transp. Syst.
**2012**, 13, 284–295. [Google Scholar] [CrossRef] - Yang, B.; Wang, Z.; Nakano, K. Effects of Penetration Rates on the Application of In-Vehicle Traffic Lights at Unsignalized Intersections. Traffic Inj. Prev.
**2022**, 23, 260–265. [Google Scholar] [CrossRef]

**Figure 2.**The deployment condition of only the preceding vehicle equipped with IVTLs: (

**a**) green light of ORTL; (

**b**) red light of ORTL.

**Figure 4.**The displacement curve of the preceding vehicle equipped with IVTLs during a green light indication from ORTLs and including three scenarios: (

**a**) both vehicles traverse the intersection; (

**b**) only the preceding vehicle traverses the intersection; (

**c**) neither the preceding vehicle nor the following vehicle traverses the intersection.

**Figure 5.**The displacement curve of the preceding vehicle equipped with IVTLs during a red light indication from the ORTL and including two scenarios: (

**a**) both vehicles traverse the intersection; (

**b**) neither the preceding vehicle nor following vehicle traverses the intersection.

**Figure 6.**The deployment condition of only the following vehicle equipped with IVTLs: (

**a**) green light of the ORTL; (

**b**) red light of the ORTL.

**Figure 8.**The displacement curve of the following vehicle equipped with IVTLs during a green light indication from the ORTL and including two scenarios: (

**a**) both vehicles traverse the intersection; (

**b**) neither the preceding vehicle nor following vehicle traverses the intersection.

**Figure 9.**The displacement curve of only the following vehicle equipped with IVTLs during a red light indication from the ORTL and including two scenarios: (

**a**) both vehicles traverse the intersection; (

**b**) neither the preceding vehicle nor following vehicle traverses the intersection.

**Figure 12.**Illustrative visualizations of the simulated scenarios: (

**a**) natural driving conditions with the preceding vehicle equipped with IVTLs; (

**b**) foggy environment with the following vehicle equipped with IVTL; (

**c**) the ORTLs blocked by a truck with the preceding vehicle equipped with IVTLs. (I) The perspective of the preceding vehicle; (II) the perspective of the following vehicle.

**Figure 13.**Outcome of minTTC derived from the designed external environment and internal conditions: (

**a**) outcome of minTTC in the scenario of sunny weather; (

**b**) outcome of minTTC in the scenario of foggy weather; (

**c**) outcome of minTTC in the scenario of being blocked by a truck.

**Figure 14.**Outcome of travel time from the designed external environment and internal conditions: (

**a**) outcome of travel time in the scenario of sunny weather; (

**b**) outcome of travel time in the scenario of foggy weather; (

**c**) outcome of travel time in the scenario of being blocked a truck.

**Figure 15.**Outcome of maxS from the designed external environment and internal conditions: (

**a**) outcome of maxS in the scenario of sunny weather; (

**b**) outcome of maxS in the scenario of foggy weather; (

**c**) outcome of maxS in the scenario of being blocked by a truck.

**Figure 16.**Outcome of maxP from the designed external environment and internal conditions: (

**a**) outcome of maxP in the scenario of sunny weather; (

**b**) outcome of maxP in the scenario of foggy weather; (

**c**) outcome of maxP in the scenario of being blocked by truck.

No. | Weather | Intersection | Preceding | Following |
---|---|---|---|---|

1 | / | / | Unequipped | Unequipped |

1-1 | / | / | Equipped | Unequipped |

1-2 | / | / | Unequipped | Equipped |

1-3 | / | / | Equipped | Equipped |

2 | Foggy | / | Unequipped | Unequipped |

2-1 | Foggy | / | Equipped | Unequipped |

2-2 | Foggy | / | Unequipped | Equipped |

2-3 | Foggy | / | Equipped | Equipped |

3 | / | Blocked by truck | Unequipped | Unequipped |

3-1 | / | Blocked by truck | Equipped | Unequipped |

3-2 | / | Blocked by truck | Unequipped | Equipped |

3-3 | / | Blocked by truck | Equipped | Equipped |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhang, Y.; Xie, Q.; Gao, M.; Guo, Y.
The Impact of In-Vehicle Traffic Lights on Driving Characteristics in the Presence of Obstructed Line-of-Sight. *Sustainability* **2023**, *15*, 8416.
https://doi.org/10.3390/su15108416

**AMA Style**

Zhang Y, Xie Q, Gao M, Guo Y.
The Impact of In-Vehicle Traffic Lights on Driving Characteristics in the Presence of Obstructed Line-of-Sight. *Sustainability*. 2023; 15(10):8416.
https://doi.org/10.3390/su15108416

**Chicago/Turabian Style**

Zhang, Yunshun, Qishuai Xie, Minglei Gao, and Yuchen Guo.
2023. "The Impact of In-Vehicle Traffic Lights on Driving Characteristics in the Presence of Obstructed Line-of-Sight" *Sustainability* 15, no. 10: 8416.
https://doi.org/10.3390/su15108416