This paper proposes an integrated lane-change trajectory planning method for advanced driver assistance system of connected and automated vehicles. First, the time-based quintic polynomial automated lane-change model is presented, which could adjust longitudinal and lateral velocity simultaneously. By tuning the lane-change duration and longitudinal displacement in the lane-change model, the lane-change reference trajectories satisfying the demands of safety, lane-change duration, travel distance, and comfort were derived under traffic-free condition. All feasible reference trajectories compose a trajectory map, which includes different driving situations, such as quick and comfortable or sudden and safe lane change. Second, the lane-change constraints induced by surrounding vehicles are introduced. The effects of surrounding vehicles on the lane-change performance are investigated by adjusting the speeds and initial gaps of preceding and rear vehicles. In addition, the initial velocity of the host vehicle is optimized to maximize the area of the trajectory map to enable a safer lane change. Finally, within the derived trajectory map, an optimal lane-change trajectory eliminating potential collisions is calculated by minimizing the lane-change duration, travel distance, driving comfort, and fuel consumption.

Much research related to negative Poisson’s ratio (NPR), or auxetic, structures is emerging these days. Several types of 3D NPR structure have been proposed and studied, but almost all of them had cuboid shapes, which were not suitable for certain engineering applications. In this paper, a cylindrical NPR structure was developed and researched. It was expected to be utilized in springs, bumpers, dampers and other similar applications. For the purpose of parametric analysis, a method of parametric modeling of cylindrical NPR structures was developed using MATLAB scripts. The scripts can automatically establish finite element models, invoke ABAQUS, read results etc. Subsequently the influences of structural parameters, including number of cells, number of layers and layer heights, on the uniaxial compression behavior of cylinder NPR structures were researched. This led to the conclusion that the stiffness of the cylindrical NPR structure was enhanced on increasing the number of cells and reducing the effective layer height. Moreover, small numbers of layers resulted in a late transition area of the load–displacement curve from low stiffness to high stiffness. Moreover, the middle contraction regions were more apparent with larger numbers of cells, smaller numbers of layers and smaller effective layer heights. The results indicate that the structural parameters had significant effects on the load–displacement curves and deformed shapes of cylindrical NPR structures. This paper is conducive to the further engineering applications of cylindrical NPR structures.

Hybrid powertrain technologies are successful in the passenger car market and have been actively developed in recent years. Optimal topology selection, component sizing, and controls are required for competitive hybrid vehicles, as multiple goals must be considered simultaneously: fuel efficiency, emissions, performance, and cost. Most of the previous studies explored these three design dimensions separately. In this paper, two novel frameworks combining these three design dimensions together are presented and compared. One approach is nested optimization which searches through the whole design space exhaustively. The second approach is called enhanced iterative optimization, which executes the topology optimization and component sizing alternately. A case study shows that the later method can converge to the global optimal design generated from the nested optimization, and is much more computationally efficient. In addition, we also address a known issue of optimal designs: their sensitivity to parameters, such as varying vehicle weight, which is a concern especially for the design of hybrid buses. Therefore, the iterative optimization process is applied to design a robust multi-mode hybrid electric bus under different loading scenarios as the final design challenge of this paper.

This paper presents an adaptive leading cruise control strategy for the automated vehicle (AV) and first considers its impact on the following human-driven vehicle (HDV) with diverse driving characteristics in the unified optimization framework for improved holistic energy efficiency. The car-following behaviors of HDV are statistically calibrated using the Next Generation Simulation dataset. In a typical single-lane car-following scenario where AVs and HDVs share the road, the longitudinal speed control of AVs can substantially reduce the energy consumption of the following HDV by avoiding unnecessary acceleration and braking. Moreover, apart from the objectives including car-following safety and traffic efficiency, the energy efficiencies of both AV and HDV are incorporated into the reward function of reinforcement learning (RL). The specific driving pattern of the following HDV is learned in real-time from historical speed information to predict its acceleration and power consumption in the optimization horizon. A comprehensive simulation is conducted to statistically verify the positive impacts of AV on the holistic energy efficiency of the mixed traffic flow with uncertain and diverse human driving behaviors. In freeway driving scenarios, simulation results indicate that the holistic energy efficiency is improved by an average of 6.03% and 6.41% compared to the reference control algorithms, specifically, RL without HDV consideration and model predictive control. These improvements highlight the significance of our approach in optimizing energy efficiency for mixed traffic on freeways.