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Controlled Auto-Ignition (CAI) combustion has been achieved in a production type 4-stroke multi-cylinder gasoline engine. The engine was based on a Ford 1.7L Zetec-SE 16V engine with a compression ratio of 10.3, using substantially standard components modified only in design dimensions to control the gas exchange process in order to significantly increase the trapped residuals. The engine was also equipped with Variable Cam Timing (VCT) on both the intake and exhaust camshafts. It was found that the largely increased trapped residuals alone were sufficient to achieve CAI in this engine and with VCT, a range of loads between 0.5 and 4 bar BMEP and engine speeds between 1000 and 3500 rpm were mapped for CAI fuel consumption and exhaust emissions. The measured CAI results were compared with those of Spark Ignition (SI) combustion in the same engine but with standard camshafts at the same speeds and loads.

Controlled Auto-Ignition (CAI) combustion was realised in a production type 4-stroke 4-cylinder gasoline engine without intake charge heating or increasing compression ratio. The CAI engine operation was achieved using substantially standard components modified only in camshafts to restrict the gas exchange process The engine could be operated with CAI combustion within a range of load (0.5 to 4 bar BMEP) and speed (1000 to 3500 rpm). Significant reductions in both specific fuel consumption and CO emissions were found. The reduction in NOx emission was more than 93% across the whole CAI range. Though unburned hydrocarbons were higher under the CAI engine operation. In order to evaluate the potential of the CAI combustion technology, the European NEDC driving cycle vehicle simulation was carried out for two identical vehicles powered by a SI engine and a CAI/SI hybrid engine, respectively.

Vehicle weight reduction has become one of the essential research areas in the automotive industry. It is important to perform design optimization of Body-in-White (BIW) at the concept design phase so that to reduce the development cost and shorten the time-to-market in later stages. Finite Element (FE) models are commonly used for vehicle design. However, even with increasing speed of computers, the simulation of FE models is still too time-consuming due to the increased complexity of models. This calls for the development of a systematic and efficient approach that can effectively perform vehicle weight reduction, while satisfying the stringent safety regulations and constraints of development time and cost. In this paper, an efficient BIW weight reduction approach is proposed with consideration of complex safety and stiffness performances. A parametric BIW FE model is first constructed, followed by the building of surrogate models for the responses of interest.

With the rapid development of autonomous driving technologies, the proportion of autonomous vehicles (AVs) will increase and influence road capacity. In this study, the simulation of mixed traffic flow was studied using an improved cellular automata model. Safety inter-vehicle spacing, the length of vehicles and reaction time are introduced into the cellular automata model. We delete the acceleration, deceleration and randomization rule for ideal conditions. Numerical simulations are utilized to analyze road capacity with different proportions of AVs. Road capacity is about 2200 pcu/h/lane for pure manual vehicle (MV) traffic flow and about 3600 pcu/h/lane for pure AV traffic flow. The capacity increases by 19.2% when there are 50% AVs in the traffic flow. And the capacity increases by around 63.6% due to the pure AV traffic flow.

The development of autonomous driving generally requires enormous annotated data as training input. The availability and quality of annotated data have been major restrictions in industry. Data synthesis techniques are then being developed to generate annotated data. This paper proposes a 2D data synthesis pipeline using original background images and target templates to synthesize labeled data for model training in autonomous driving. The main steps include: acquiring templates from template libraries or alternative approaches, augmenting the obtained templates with diverse techniques, determining the positioning of templates in images, fusing templates with background images to synthesize data, and finally employing the synthetic data for subsequent detection and segmentation tasks. Specially, this paper synthesizes traffic data such as traffic signs, traffic lights, and ground arrow markings in 2D scenes based on the pipeline.