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Register a new userA Cooperative Control Framework for CAV Lane Change in a Mixed Traffic Environment
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In preparing for connected and autonomous vehicles (CAVs), a worrisome aspect is the transition era which will be characterized by mixed traffic (where CAVs and human-driven vehicles (HDVs) share the roadway). Consistent with expectations that CAVs will improve road safety, on-road CAVs may adopt rather conservative control policies, and this will likely cause HDVs to unduly exploit CAV conservativeness by driving in ways that imperil safety. A context of this situation is lane-changing by the CAV. Without cooperation from other vehicles in the traffic stream, it can be extremely unsafe for the CAV to change lanes under dense, high-speed traffic conditions. The cooperation of neighboring vehicles is indispensable. To address this issue, this paper develops a control framework where connected HDVs and CAV can cooperate to facilitate safe and efficient lane changing by the CAV. Throughout the lane-change process, the safety of not only the CAV but also of all neighboring vehicles, is ensured through a collision avoidance mechanism in the control framework. The overall traffic flow efficiency is analyzed in terms of the ambient level of CHDV-CAV cooperation. The analysis outcomes are including the CAVs lane-change feasibility, the overall duration of the lane change. Lane change is a major source of traffic disturbance at multi-lane highways that impair their traffic flow efficiency. In providing a control framework for lane change in mixed traffic, this study shows how CHDV-CAV cooperation could help enhancing system efficiency.
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Originally, the decision and control of the lane change of the vehicle were on the human driver. In previous studies, the decision-making of lane-changing of the human drivers was mainly used to increase the individuals benefit. However, the lane-changing behavior of these human drivers can sometimes have a bad influence on the overall traffic flow. As technology for autonomous vehicles develop, lane changing action as well as lane changing decision making fall within the control category of autonomous vehicles. However, since many of the current lane-changing decision algorithms of autonomous vehicles are based on the human driver model, it is hard to know the potential traffic impact of such lane change. Therefore, in this study, we focused on the decision-making of lane change considering traffic flow, and accordingly, we study the lane change control system considering the whole traffic flow. In this research, the lane change control system predicts the future traffic situation through the cell transition model, one of the most popular macroscopic traffic simulation models, and determines the change probability of each lane that minimizes the total time delay through the genetic algorithm. The lane change control system then conveys the change probability to this vehicle. In the macroscopic simulation result, the proposed control system reduced the overall travel time delay. The proposed system is applied to microscopic traffic simulation, the oversaturated freeway traffic flow algorithm (OFFA), to evaluate the potential performance when it is applied to the actual traffic system. In the traffic flow-density, the maximum traffic flow has been shown to be increased, and the points in the congestion area have also been greatly reduced. Overall, the time required for individual vehicles was reduced.
This paper aims to investigate the characteristics of durations of discretionary lane changes (LCs) on freeways based on an enriched dataset containing LC vehicle trajectories of 2905 passenger cars and 433 heavy vehicles. A comprehensive analysis of LC duration is conducted and four stochastic LC duration models are built according to vehicle types and LC directions. It is found that the LC duration varies across different vehicle types and LC directions. The modelling results show that different variables have different effects on LC duration for different vehicle types and LC directions. Fixed-parameter, latent class, and random parameter accelerated hazard time (AFT) models were built considering driver heterogeneity. Results show that heavy vehicle drivers show more heterogeneity. Different variables were found for different vehicle types and LC directions. The results of this study can be beneficial to understand the mechanism of LC process and the influence of LC on traffic flow.
This paper proposes a specification-guided framework for control of nonlinear systems with linear temporal logic (LTL) specifications. In contrast with well-known abstraction-based methods, the proposed framework directly characterizes the winning set, i.e., the set of initial conditions from which a given LTL formula can be realized, over the continuous state space of the system via a monotonic operator. Following this characterization, an algorithm is proposed to practically approximate the operator via an adaptive interval subdivision scheme, which yields a finite-memory control strategy. We show that the proposed algorithm is sound for full LTL specifications, and robustly complete for specifications recognizable by deterministic Buchi automata (DBA), the latter in the sense that control strategies can be found whenever the given specification can be satisfied with additional bounded disturbances. Without having to compute and store the abstraction and the resulting product system with the DBA, the proposed method is more memory efficient, which is demonstrated by complexity analysis and performance tests. A pre-processing stage is also devised to reduce computational cost via a decomposition of the specification. We show that the proposed method can effectively solve real-world control problems such as jet engine compressor control and motion planning for manipulators and mobile robots.
Current state-of-art traffic microsimulation tools cannot accurately estimate safety, efficiency, and mobility benefits of automated driving systems and vehicle connectivity because of not considering physical and powertrain characteristics of vehicles and resistance forces. This paper proposes realistic longitudinal control functions for autonomous vehicles with and without vehicle-to-vehicle communications and a realistic vehicle-following model for human-driven vehicles, considering driver characteristics and vehicle dynamics. Conventional longitudinal control functions apply a constant time gap policy and use empirical constant controller coefficients, potentially sacrificing safety or reducing throughput. Proposed longitudinal control functions calculate minimum safe time gaps at each simulation time step and tune controller coefficients at each simulation time step during acceleration and deceleration to maximize throughput without compromising safety.
Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency. Most of the existing research on unsignalized intersection cooperation considers fixed lane direction, where only specific turning behavior of vehicles is allowed on each lane. Given that traffic volume and the proportion of vehicles with different turning expectation may change with time, fixed lane direction may lead to inefficiency at intersections. This paper proposes a multi-lane unsignalized intersection cooperation method that considers flexible lane direction. The two-dimensional distribution of vehicles is calculated and vehicles that are not in conflict are scheduled to pass the intersection simultaneously. The formation reconfiguration method is utilized to achieve collision-free longitudinal and lateral position adjustment of vehicles. Simulations are conducted at different input traffic volumes and turning proportion of incoming vehicles, and the results indicate that our method outperformances the fixed-lane-direction unsignalized cooperation method and the signalized method.
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