Wei Wang

Model Predictive Control (MPC) is increasingly gaining popularity in a wide range of autonomous vehicle and driving assistant applications owing to its style of optimality and convenience in handling multiple constraints. However, a primary drawback in online applications lies in the high computational burden caused by the complex non-linearity in the vehicle system and driving stability constraints. The differential flatness property of the vehicle motion system offers a solution by treating the error signals as flat outputs for endogenous feedback linearization. Nevertheless, this approach introduces a new challenge of dealing with time-varying implicit constraints. To address this issue, our work combines flatness-based MPC design with real-time iterative handling of lateral stability constraints, leading to a pure linear Quadratic Programming (QP) problem that can be efficiently solved online. Through numerical simulations conducted under two challenging scenarios, we demonstrate that our proposed method outperforms conventional linear-time-varying MPC in terms of linearization error and dynamic performance. Finally, field tests conducted with a four-wheel independent driving and four-wheel independent steering chassis platform validate the feasibility and substantial potential of our presented method in autonomous driving applications.

The wheel-side electric drive system is a melding of a vehicle powertrain and suspension system, which saves chassis space and can adapt to different models. To achieve the goal of highly modularized development, the system is supposed to meet the requirements of various working conditions without changing the interface state. The electric motor drives the wheel through two-stage fixed axis helical gears, so the transmission is short in path and acts as the suspension arm at the same time. As a result, the gears are critical to output robustness and NVH performance. The modeling accuracy is decisive for simulations and tests, so it is necessary to build a precise geometric model instead of the data-fitting estimation. The gears are manufactured by a hobbing and form grinding process, which is described functionally along with the relationship between the tooling parameters and tooth profile curves. Based on the rain flow methodology and extrapolation theory, a comprehensive load spectrum with nine stages is formulated, which can cover the working conditions of a basic version, a NVH version, and a durability version. According to the Miner cumulative damage hypothesis, the equivalent durability mileage of 150,000 km is converted. The prototype machine is simulated and verified on the test bench, and the test results show that the wheel-side electric drive system has a reliable output performance. The equivalent damage of the comprehensive load spectrum is 63.27%, where the 2# stage driving gear is the most vulnerable component of the whole system. The research in this paper can provide data support for damage calculation and lightweight optimization with modularized development and applications in the future.

This paper presents a novel power electronic module (PEM) for chassis-by-wire in passenger cars. The PEM is supposed to be installed in a close-to-wheel position, which provides electrical interfaces with a power harness and signal harness. When the vehicle is driving, the PEM works as a dynamic vibration absorber (DVA) to diminish the negative effects of un-sprung mass. Based on the vibration system model, the mechanical principles are analyzed and the design parameters are mathematically optimized. For a comparison of different configuration schemes with an in-wheel motor (IWM), we take the condition of a vehicle driving at a speed of 15 m/s on a C-class road to examine indicators of vehicle body acceleration, wheel dynamic load, and suspension dynamic deflection. The calculation results prove that the system has advantages in ride comfort and wheel grounding characteristics. For the detailed design of the machine, we build a digital virtual prototype for simulation. Compared to the initial state, the optimized DVA configuration has obvious suppression in component vibration, including the vehicle body, the IWM, and the PEM. The frequency sweep analysis proves a robust result, which ensures that the frequency and amplitude are both within the vibration tolerance range of PEM operations.

Traffic safety is one of the vital issues in developing autonomous and assisted driving systems. To achieve higher level of driving automation, it becomes necessary to have a reliable Risk Assessment (RA) method that not only responds to current traffic conditions but also anticipates future scenario propagation. Regardless of the driver intentions, traditional deterministic risk indicators like Time-To-Collision (TTC) have proven effective but fall short in addressing the inherent uncertainty in future propagation, especially under conflict scenarios influenced by interdependent decisions of traffic participants. Acceleration for Collision Avoidance (ACA) emerges as a flexible risk criterion adaptable to different collision-avoidance maneuvers. Focusing on the Left/Right Turn across path conflicts, this work aims to propose an innovative surrogate risk indicator for collision avoidance with the combination of ACA criteria and scenario classification using Hidden Markov Models (HMM). Based on a near-miss video database, we further train and evaluate the presented model, supplying interpretability and adaptability of risk assessment in complex conflict scenarios.

The extra unsprung mass introduced by the propulsion motors becomes an unavoidable issue for electric wheel drive systems, resulting in deteriorated ride comfort and tire adhesion stability. This study presents an innovative wheel drive system based on the dynamic vibration absorber (DVA) to suppress the unsprung mass vibration, improving the suspension dynamic performance. Specifically, we apply the principle of adaptive transmission to generate an extra degree of freedom (DoF), enabling the propulsion motor to work as the DVA. Innovative solutions with different topologies are exploited and an appropriate one is selected for a specific chassis platform. Based on the quarter-suspension dynamics with 3 DoF, the optimal design regarding the component sizing and stiffness/damping characteristics of unsprung mass suspension is carried out to minimize the vertical body acceleration and dynamic tire load through the genetic algorithm. In practical application, bench tests of the prototype prove that the proposed electric wheel drive system has reasonable driving efficiency and torque tracking performance, which verifies the feasibility of the proposed structure.