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The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

The aviation sector has played a significant role in shaping the world into what it is today. The rapid growth of global economies and the corresponding sharp rise in the number of people now wanting to travel on business and for pleasure, has largely been responsible for the development of this industry. With a predicted rise in Revenue Passenger Kilometers (RPK) by over 150% in the next 20 years, the industry will correspondingly be a significant contributor to environmental emissions. Under such circumstances optimizing aircraft trajectories for lowered emissions will play a critical role amongst various other measures, in mitigating the probable environmental effects of increased air traffic. Aircraft trajectory optimization using evolutionary algorithms is a novel field and preliminary studies have indicated that a reduction in emissions is possible when set as objectives.

Testing real-time vehicular systems challenges the tester to design test cases for concurrent and sequential input events, emulating unexpected user and usage profiles. The vehicle response should be robust to unexpected user actions. Sequence Covering Arrays (SCA) offer an approach which can emulate such unexpected user actions by generating an optimized set of test vectors which cover all possible t-way sequences of events. The objective of this research was to find an efficient nonfunctional sequence testing (NFST) strategy for testing the robustness of real-time automotive embedded systems measured by their ability to recover (prove-out test) after applying sequences of user and usage patterns generated by combinatorial test algorithms, considered as “noisy” inputs. The method was validated with a case study of an automotive embedded system tested at Hardware-In-the-Loop (HIL) level. The random sequences were able to alter the system functionality observed at the prove-out test.

This paper describes and demonstrates the use of an assembly centric design algorithm as an aid to achieving minimal hard tooling assembly concepts. The algorithm consists of a number of logically ordered design methodologies and also aids the identification of other enabling technologies. Included in the methodologies is an innovative systems analysis tool that enables the comparison of alternative assembly concepts, and the prediction and control of the total assembly error, at the outline stage of the design.

As the essential of future driver assistance system, brake-by-wire system is capable of performing autonomous intervention to enhance vehicle safety significantly. Regenerative braking is the most effective technology of improving energy consumption of electrified vehicle. A novel brake-by-wire system scheme with integrated functions of active braking and regenerative braking, is proposed in this paper. Four pressure-difference-limit valves are added to conventional four-channel brake structure to fulfill more precise pressure modulation. Four independent isolating valves are adopted to cut off connections between brake pedal and wheel cylinders. Two stroke simulators are equipped to imitate conventional brake pedal feel. The operation principles of newly developed system are analyzed minutely according to different working modes. High fidelity models of subsystems are built in commercial software MATLAB and AMESim respectively.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

The vehicle dynamics control systems are traditionally based upon utilizing wheel brakes as actuators. However, there has been recently strong interest in the automotive industry for introduction of other vehicle dynamics actuators, in order to improve the overall vehicle stability, responsiveness, and agility features. This paper considers various actuators such as active rear and central differentials and active front and rear steering, and proposes design of related yaw rate control systems. Different control subsystems such as reference model, feedback and feedforward control, allocation algorithm, and time-varying controller limit are discussed. The designed control systems are verified and compared by computer simulation for double lane change and slalom maneuvers.

Significant effort has been applied to the introduction of automation for the structural assembly of aircraft. However, the equipping of the aircraft with internal services such as hydraulics, fuel, bleed-air and electrics and the attachment of movables such as ailerons and flaps remains almost exclusively manual and little research has been directed towards it. The problem is that the process requires lengthy assembly methods and there are many complex tasks which require high levels of dexterity and judgement from human operators. The parts used are prone to tolerance stack-ups, the tolerance for mating parts is extremely tight (sub-millimetre) and access is very poor. All of these make the application of conventional automation almost impossible. A possible solution is flexible metrology assisted collaborative assembly. This aims to optimise the assembly processes by using a robot to position the parts whilst an operator performs the fixing process.

In today's aircraft the diagnostic and prognostic systems play a crucial part in aircraft safety while reducing the operating and maintenance costs. Aircraft are very complex in their design and require consistent monitoring of systems to establish the overall vehicle health status. Most diagnostic systems utilize advanced algorithms (e.g. Bayesian belief networks or neural networks) which usually operate at system or sub-system level. The sub-system reasoners collect the input from components and sensors to process the data and provide the diagnostic/detection results to the flight advisory unit. Several sources of information must be taken into account when assessing the vehicle health, to accurately identify the health state in real time. These sources of information are independent system-level diagnostics that do not exchange any information/data with the surrounding systems.

Every state-of-art aircraft has a complex distributed systems of avionics Line Replaceable Units/Modules (LRUs/LRMs), networked by several Data buses. These LRUs are becoming more complex because of an increasing number of new functions need to be integrated into avionics architecture. Moreover, the complexity of the overall avionics architecture and its impact on cable length, weight, power consumption, reliability and maintainability of avionics systems encouraged manufacturers to incorporate efficient avionics architectures in their aircraft design process. The evolution of avionics data buses and architectures have moved from distributed analog and federated architecture to digital integrated modular avionics (IMA). IMA architecture allows suppliers to develop their own LRUs/LRMs capable of specific features that can then be offered to Original Equipment Manufacturers (OEMs) as Commercial-Off-The-Shelf (COTS) products.