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Hybridization of vehicles is considered as the most promising technology for automakers and researchers, facing the challenge of optimizing both the fuel economy and emission of the road transport. Extensive studies have been performed on power-split hybrid electric vehicles (PS-HEVs). Despite of the fact that their excellent fuel economy performance in city driving conditions has been witnessed, a bottle neck for further improving the fuel economy of PS-HEVs has been encountered due to the inherent engine-generator-motor power circulation of the power-split system under medium-low speed cruising scenarios. Due to the special mechanical constraints of the power-split device (PSD), the conventional periodic cruising strategy like Pulse and Glide cannot be applied to PS-HEVs directly.

Vehicle automation is a fundamental approach to reduce traffic accidents and driver workload. However, there is a notable risk of pushing human drivers out of the control loop before automation technology fully matures. Cooperative driving (or vehicle co-piloting) is a novel paradigm which is defined as the vehicle being jointly navigated by a human driver and an automatic controller through shared control technology. Indirect shared control is an emerging shared control method, which is able to realize cooperative driving through input complementation instead of haptic guidance. In this paper we first establish an indirect shared control method, in which the driver’s commanded input and the controller’s desired input are balanced with a weighted summation. Thereafter, we propose a predictive model to capture driver adaptation and trust in indirect shared control.

The reference path played a very important role in the parking schemes. In this paper, an arc tangent liked polynomial trajectory model is proposed, and an optimal trajectory is obtained for automatic parallel parking based on genetic algorithm, which ensures that the vehicle does not collide with obstacles or other vehicles during parking. The proposed algorithm has strong robustness because of that all the parameters of the vehicle and the parallel parking spaces are parameterized. Using the trajectory model with the vehicle and parking space parameters, a cost function with multi-constraints, were established for path planning. The start and end points of the planning trajectory are the actual starting point and the desired final parking point of the vehicle by choosing three parameters of the trajectory model appropriately. Simulation results illustrate the effectiveness of the proposed algorithm.