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Threat assessment (TA) method is vital in the decision-making process of intelligent vehicles (IVs), especially for ADAS systems. In the research of TA, the probabilistic threat assessment (PTA) method is acting an increasing role, which can reduce the uncertainties of driver’s maneuvers. However, the driver behavior model (DBM) used in present PTA methods was mainly constructed by limited data or simple functions, which is not entirely reasonable and may affect the performance of the TA process. This work aims to utilize crash data extracted from Event Data Recorder (EDR) to establish more accurate DBM and improve the current PTA method in rear-end situations. EDR data with responsive maneuvers were firstly collected, which were then employed to construct the initial DBM (I-DBM) model by using the multivariate Gaussian distribution (MGD) framework. Besides, the model was further subdivided into six parts by two important risk indicators, Time-to-collision (TTC) and velocity.

Suspension system dynamics can be obtained by various methods and vehicle design has gained great advantages over the dynamics analysis. By employing the new Udwadia-Kalaba equation, we endeavor some attempts on its application to dynamic modeling of vehicle suspension systems. The modeling approach first segments the suspension system into several component subsystems with kinematic constraints at the segment points released. The equations of motion of the unconstrained subsystems are thus easily obtained. Then by applying the second order constraints, the suspension system dynamics is then obtained. The equations are of closed-form. Having the equations obtained, we then show its application on dynamical load analysis. The solutions for the dynamical loads at interested hard points are obtained. We use the double wishbone suspension to show the systematic approach is easy handling.

Uncertainties present a large concern in actual vehicle motion and have a large effect on vehicle system control. We attempt a new robust control design approach for braking/driving force distribution and active front steering of vehicle system with uncertain parameters. The braking/driving force distribution control is equivalently studied as the integral direct yaw moment control. Then the control design is carried out by using a state-space vehicle model with embedded fuzzy uncertainties. By taking the compensated front wheel steering angle and the direct yaw moment as the control inputs, a feedback control that aims to compensate the system uncertainty is proposed. In a quite different angle, we employ fuzzy descriptions of the uncertain parameters. The controlled system performance is deterministic, and the control is not if-then rules-based. Fuzzy descriptions of the uncertain parameters are used to find an optimal control gain.

An intelligent connected vehicle (ICV) swarm system that includes N vehicles is considered. Based on the special properties of potential functions, a kinematic model describing the swarm performances is proposed, which allows all vehicles to enclose the tracking target and show both tracking and formation characteristics. Treating the performances as the desired constraints, the analytical form of constraint forces can be obtained inspired by the Udwadia-Kalaba approaches. A special approach of uncertainty decomposition to deal with uncertain interferences is proposed, and a switching-type robust control method is addressed for each vehicle agent in the swarm system. The features and validity of the addressed control are demonstrated in the numerical simulations.

The intelligent unmanned ground vehicle (UGV) motorcade system consisting of one leader and n − 1 followers is considered. The safety distance between the front and rear UGVs is treated as the control target. Since the safety distance constraint is a unilateral constraint, the state transformation is needed. Hence, a piecewise type conversion function is formulated to serve for the transformation of the original inequality constraint. The system equation is further expressed by the new state. We assume that the input of the leading UGV is known. Combined with the uncertainty evaluation, a class of collaborative controls for the following UGVs is proposed to deal with the uncertainty with unknown bound. The effectiveness of the designed control is verified by both Lyapunov stability theory and simulations. Both theoretical and simulation results illustrate that the longitudinal safety, stability and global behavior of the intelligent motorcade system are guaranteed.