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In order to establish the correlation between objective and subjective evaluation of brake pedal feel for passenger cars, road tests of brake pedal feel were carried out and an evaluation method was proposed. In the road tests, subjective scores and objective measurements were obtained under the conditions of uniform and emergency braking. The objective measurements include pedal preload, low deceleration pedal force and travel, moderate deceleration pedal force and travel, brake response time and brake linearity. Using the theory of analytic hierarchy process (AHP), the design process of the evaluation method was established. Key setups including the hierarchical structure model, the judgement matrix and the score calculation method of objective measurements were described in detail. Then, the correlation between subjective and objective scores was analyzed. It can be concluded that the evaluation method is effective and it can be applied to brake pedal feel assessment and adjustment.

Durability and performance evaluation of the ageing catalyzed continuously regenerating trap (CCRT) on solid and volatile particles from diesel bus were studied through a set of transient TSI engine exhaust particle sizer spectrometer based on on-road test. Particle characteristics under stepped steady conditions and during regeneration were discussed in detail. Under idle and stepped steady conditions, total particle number and mass Emission Rate (ER) of each test presented rising trends as speed increase. Total number ERs of all tests showed downtrend as the CCRT aging. The particle number size distributions at different ageing stage showed changing characteristics due to developing filter mechanism. Compared with baseline data, the total number reduction rates at idle condition were incremental, from 91.4% to 98.9% as the CCRT ageing. Percentages of nuclei mode concentrations took higher range from 66.6% to 89.9% compared with the baseline data, 43.2-43.7%.

Being one of the most simple and basic driving scenarios, highway scenario can be one of the first scenarios to achieve autonomous driving. Both car following (CF) and lane changing (LC) are the most basic and frequent maneuver during highway driving tasks, and therefore become two key issues to focus on in recent researches about autonomous vehicle (AV). Different from conventional CF and LC researches that attach much importance to the character, psychology, perception ability, and driving experience of human drivers, more timely and accurate reactions based on fast perception and communication technology as well as the automatic actuator are hypotheses for this research. Moreover, based on these hypotheses, a modified intelligent driver model (MIDM) is proposed for AVs’ following behavior to alleviate the fluctuations caused by lane changing behaviors.

An adaptive sideslip angle observer based on discrete extended Kalman filter (DEKF) is proposed in this paper and tire-road friction adaptation is also considered. The single track vehicle model with nonlinear tire characteristics is adopted. The tire parameters can be easily obtained through road test data without using special test rig. Afterwards, this model is discretized and the maximum value of tire-road friction is modeled as the third state variable. Through the measurement of vehicle lateral acceleration and yaw rate, the tire-road adhesion coefficient can be timely updated. Simulations with experimental data from road test and driving simulator have confirmed that DEKF has very high accuracy. The convergent speed of DEKF relies on the magnitude of lateral excitation.

This workbook, a companion to the book Road Vehicle Dynamics, will enable students and professionals from a variety of disciplines to engage in problem-solving exercises based on the material covered in each chapter of that book. Emphasizing application more than theory, the workbook presents systematic rules of analysis that students can follow in a step-by-step manner to understand the efficiencies or shortcomings of various techniques. Readers will gain a greater understanding of the factors influencing ride, handling, braking, acceleration, and vehicle safety.

Connected and autonomous vehicles are different from traditional vehicles. The communication between vehicles (V2V) or between vehicles and infrastructures (V2I) renders it possible to convey traffic information (e.g. signal timing or speed advisory) from signal controllers to vehicles as well as vehicles to vehicles in real time. Taking this advantage, this paper aims to developing an algorithm which enables the interconnected autonomous vehicles running efficiently on arterials. A set of driving rules determining random behavior and swarm behavior of autonomous vehicles is developed based on swarm intelligence theory. Under control of these rules, each autonomous vehicle follows the same rules, which make it select target vehicle from all the optimal individuals in detection zone according to characteristics of itself, then approach to the target by changing lane, following former car, or accelerating.