A co-evolutionary lane-changing trajectory planning method for automated vehicles based on the instantaneous risk identification.

Lane-changing trajectory planning (LTP) is an effective concept to control automated vehicles (AVs) in mixed traffic, which can reduce traffic conflicts and improve overall traffic efficiency. To enhance the lane change safety for AVs, a co-evolutionary lane-changing trajectory planning (CLTP) method is proposed to describe the risk minimization process that co-evolves with the dynamic traffic environment in the limited literature. Firstly, the natural driving data of vehicle trajectory on the expressway provided by the High dataset are used to construct the lane-changing samples. To obtain the future traffic environment information, a deep learning neural network is adopted to capture trajectory dynamics in mobility of surrounding vehicles around a lane-changing vehicle. Secondly, the safe interaction between the subject vehicle and the surrounding vehicles is considered to establish a mathematical model for the temporal and spatial risk identification of a lane change event based on the fault tree analysis method. Subsequently, the risk minimization of lane change is considered as the objective. Based on the acceleration and deceleration overtaking rules and the trapezoidal acceleration method, the longitudinal and lateral displacement schemes during a lane change are designed. Finally, the motion parameters of longitudinal and lateral displacement are acquired to form an ideal lane change trajectory using a genetic algorithm. The results show that this method can effectively achieve higher safety of the lane-changing process, and reduce the traffic conflicts and traffic turbulence caused by dangerous lane-changing behaviors. The findings can provide theoretical support for lane change trajectory planning algorithm design of intelligent vehicles.

Gradient illumination scheme design at the highway intersection entrance considering driver's light adaption.

OBJECTIVE: Improving lighting arrangements at the highway intersections can significantly reduce the likelihood of crashes. Meanwhile, a reasonable gradient illumination scheme can increase drivers' safety by avoiding the rapid change of their pupil area when driving from an unlighted area into a lighted area. The purpose of this study is to design illumination transition zones for drivers when approaching the highway intersection, and optimize the illumination increments along the transition zones. METHODS: This issue is addressed in three stages. First, an indoor simulation platform is built using the UC-win/road software, and its parameters are calibrated using real intersection data collected at night. Second, the variation of pupil area under different increasing rates of illuminance near the driver's eyes (INDE) is analyzed, and a model representing the temporal change of the area in the pupil contraction stage is established. Last, an optimization model is proposed to obtain the optimal luminance increasing rates by minimizing the time needed for drivers to travel through the transition zones under different maximum illuminations and speed limits. RESULTS: The findings of this study indicate that the INDE increasing time in the transition zone remains unchanged with a determined illumination at the intersection, and the optimal transition zone length is directly proportional to the speed limit of the intersection. Therefore, the transition zone length needs to be adjusted to accommodate different speed limits: it should be extended or shortened by the same percentage as the increase or decrease of the speed limit. CONCLUSIONS: It can be concluded that the illumination transition zone is necessary when the INDE of highway intersection and the speed limit exceed 8 lx and 40 km/h, respectively. The INDE increasing rate should be maintained around the optimal value, providing safe and comfortable light adaptation to drivers and preventing traffic accidents. The study will provide a scientific basis for safety implementation of lighting arrangements at highway intersections.

A real-time traffic control method for the intersection with pre-signals under the phase swap sorting strategy.

To deal with the conflicts between left-turn and through traffic streams and increase the discharge capacity, this paper addresses the pre-signal which is implemented at a signalized intersection. Such an intersection with pre-signal is termed as a tandem intersection. For the tandem intersection, phase swap sorting strategy is deemed as the most effective phasing scheme in view of some exclusive merits, such as easier compliance of drivers, and shorter sorting area. However, a major limitation of the phase swap sorting strategy is not considered in previous studies: if one or more vehicle is left at the sorting area after the signal light turns to red, the capacity of the approach would be dramatically dropped. Besides, previous signal control studies deal with a fixed timing plan that is not adaptive with the fluctuation of traffic flows. Therefore, to cope with these two gaps, this paper firstly takes an in-depth analysis of the traffic flow operations at the tandem intersection. Secondly, three groups of loop detectors are placed to obtain the real-time vehicle information for adaptive signalization. The lane selection behavior in the sorting area is considered to set the green time for intersection signals. With the objective of minimizing the vehicle delay, the signal control parameters are then optimized based on a dynamic programming method. Finally, numerical experiments show that average vehicle delay and maximum queue length can be reduced under all scenarios.

Comparative analysis of driver's brake perception-reaction time at signalized intersections with and without countdown timer using parametric duration models.

Countdown timers display the time left on the current signal, which makes drivers be more ready to react to the phase change. However, previous related studies have rarely explored the effects of countdown timer on driver's brake perception-reaction time (BPRT) to yellow light. The goal of this study was therefore to characterize and model driver's BPRT to yellow signal at signalized intersections with and without countdown timer. BPRT data for "first-to-stop" vehicles after yellow onset within the transitional zone were collected through on-site observation at six signalized intersections in Harbin, China. Statistical analysis showed that the observed 15th, 50th, and 85th percentile BPRTs without countdown timer were 0.52, 0.84, and 1.26s, respectively. The observed 15th, 50th, and 85th percentile BPRTs with countdown timer were 0.32, 1.20, and 2.52s, respectively. Log-logistic distribution appeared to best fit the BPRT without countdown timer, while Weibull distribution seemed to best fit the BPRT with countdown timer. After that, a Log-logistic accelerated failure time (AFT) duration model was developed to model driver's BPRT without countdown timer, whereas a Weibull AFT duration model was established to model driver's BPRT with countdown timer. Three significant factors affecting the BPRT identified in both AFT models included yellow-onset distance from the stop line, yellow-onset approach speed, and deceleration rate. No matter whether the presence of countdown timer or not, BPRT increased as yellow-onset distance to the stop line or deceleration rate increased, but decreased as yellow-onset speed increased. The impairment of driver's BPRT due to countdown timer appeared to increase with yellow-onset distance to the stop line or deceleration rate, but decrease with yellow-onset speed. An increase in driver's BPRT because of countdown timer may induce risky driving behaviors (i.e., stop abruptly, or even violate traffic signal), revealing a weakness of countdown timer in traffic safety aspect.