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This study aims to propose a centralized optimal control model for automated left-turn platoon at contraflow left-turn lane (CLL) intersections.

The lateral lane change control and the longitudinal acceleration in the control horizon are optimized simultaneously with the objective of maximizing traffic efficiency and smoothness. The proposed model is cast into a mixed-integer linear programming problem and then solved by the branch-and-bound technique.

The proposed model has a promising control effect under different geometric controlled conditions. Moreover, the proposed model performs robustly under various safety time headways, lengths of the CLL and green times of the main signal.

This study proposed a centralized optimal control model for automated left-turn platoon at CLL intersections. The lateral lane change control and the longitudinal acceleration in the control horizon are optimized simultaneously with the objective of maximizing traffic efficiency and smoothness

This paper aims to use active fine lane management methods to solve the problem of congestion in a weaving area and provide theoretical and technical support for traffic control under the environment of intelligent connected vehicles (ICVs) in the future.

By analyzing the traffic capacities and traffic behaviors of domestic and foreign weaving areas and combining them with field investigation, the paper proposes the active and fine lane management methods for ICVs to optimal driving behavior in a weaving area. The VISSIM simulation of traffic flow vehicle driving behavior in weaving areas of urban expressways was performed using research data. The influence of lane-changing in advance on the weaving area was evaluated and a conflict avoidance area was established in the weaving area. The active fine lane management methods applied to a weaving area were verified for different scenarios.

The results of the study indicate that ICVs complete their lane changes before they reach a weaving area, their time in the weaving area does not exceed the specified time and the delay of vehicles that pass through the weaving area decreases.

Based on the vehicle group behavior, this paper conducts a simulation study on the active traffic management control-oriented to ICVs. The research results can optimize the management of lanes, improve the traffic capacity of a weaving area and mitigate traffic congestion on expressways.

Precise vehicle localization is a basic and critical technique for various intelligent transportation system (ITS) applications. It also needs to adapt to the complex road environments in real-time. The global positioning system and the strap-down inertial navigation system are two common techniques in the field of vehicle localization. However, the localization accuracy, reliability and real-time performance of these two techniques can not satisfy the requirement of some critical ITS applications such as collision avoiding, vision enhancement and automatic parking. Aiming at the problems above, this paper aims to propose a precise vehicle ego-localization method based on image matching.

This study included three steps, Step 1, extraction of feature points. After getting the image, the local features in the pavement images were extracted using an improved speeded up robust features algorithm. Step 2, eliminate mismatch points. Using a random sample consensus algorithm to eliminate mismatched points of road image and make match point pairs more robust. Step 3, matching of feature points and trajectory generation.

Through the matching and validation of the extracted local feature points, the relative translation and rotation offsets between two consecutive pavement images were calculated, eventually, the trajectory of the vehicle was generated.

The experimental results show that the studied algorithm has an accuracy at decimeter-level and it fully meets the demand of the lane-level positioning in some critical ITS applications.

Fatigue associated with shift work is a well-established and pervasive problem in policing that affects officer performance, safety, and health. It is critical to understand the extent to which fatigue degrades officer driving performance. Drowsy driving among post-shift workers is a well-established risk factor yet no data are available about officer injuries and deaths due to drowsy driving. The purpose of this paper is to assess the impact of fatigue associated with work shift and prior sleep on officers’ non-operational driving using laboratory experiments to assess post-shift drowsy driving risks and the ability of a well-validated vigilance and reaction-time task to assess these risks.

Experienced police patrol officer volunteers (n=78) from all four shifts of a medium-sized city’s police department were tested using a within- and between-subjects design to assess the impact of fatigue on individual officers, as well as the impact of different work shifts, on post-shift driving performance. Controlled laboratory experiments were conducted during which participants drove high-fidelity driving training simulators on two occasions: immediately following five consecutive 10:40-hour patrol shifts (fatigued condition) and again 72 hours after completing the last shift in a work cycle (rested condition).

Generalized linear mixed-model analyses of driving performance showed that officers working night shifts had significantly greater lane deviation during post-shift, non-operational driving than those working day shifts (F=4.40, df=1, 150, p=0.038). The same method also showed that easy to measure psychomotor vigilance test scores for reaction time predicted both lane deviation (F=31.48, df=1, 151, p < 0.001) and collisions (F=14.10, df=1, 151, p < 0.001) during the simulated drives.

Simulated driving tasks done by participants were generally less challenging than patrol or off-duty driving and likely underestimate the impact of fatigue on police driving post-shift or during extended shifts.

This is the first experimental research to assess the impact of shiftwork, fatigue, and extended shifts on police post-shift drowsy driving, a known risk factor for shift workers in general.