Application of Recursive Least Square Algorithm on Estimation of Vehicle Sideslip Angle and Road Friction

A recursive least square (RLS) algorithm for estimation of vehicle sideslip angle and road friction coefficient is proposed. The algorithm uses the information from sensors onboard vehicle and control inputs from the control logic and is intended to provide the essential information for active safety systems such as active steering, direct yaw moment control, or their combination. Based on a simple two-degree-of-freedom (DOF) vehicle model, the algorithm minimizes the squared errors between estimated lateral acceleration and yaw acceleration of the vehicle and their measured values. The algorithm also utilizes available control inputs such as active steering angle and wheel brake torques. The proposed algorithm is evaluated using an 8-DOF full vehicle simulation model including all essential nonlinearities and an integrated active front steering and direct yaw moment control on dry and slippery roads

Driving risk assessment and prevention strategies for autonomous vehicle in open-pits

Driving risk assessment and protection is the critical technology of unmanned transportation systems in open-pits. In order to warrant the safe operation of unmanned vehicles in open-pits, the Driving Security Model (DSM) based on the vehicle-road-cloud transportation system is established. Based on the multi-source information from the vehicle, roadside, and cloud platform, the DSM can assess the driving risk level of driverless vehicles and provide corresponding driving risk prevention strategies. The DSM comprises driving state awareness, driving risk assessment, and driving risk protection. In terms of driving risk assessment, the threshold of pre-collision time is corrected through the road slope ahead of the vehicle, and the minimum braking safety distance is modified by the information of road slope and vehicle load state. In the meantime, a comprehensive driving risk assessment strategy is proposed, which can quantify the real-time collision risk of autonomous vehicles in open-pits. Then, a collision risk protection system that considers different driving risks is then designed based on a finite state machine. A smooth braking control strategy is developed to meet the minimum safety distance. Finally, a digital twin simulation system that corresponds to the autonomous vehicle in an open-pit is built based on the PreScan and Matlab co-simulation technology and some simulation tests in the horizontal, uphill-downhill road and full load scenes are carried out. The simulation results show that the DSM’s comprehensive risk assessment strategy can evaluate suitable risk levels in advance and timely brake, which indicates that the introduction of road slope information can improve the driving safety of the vehicle up and downhill scenes. By introducing vehicle load information, the designed minimum safe braking distance index can detect potential collision risk in time. The DSM’s emergency braking control strategy can smoothly stop the vehicle before 10 m safe distance, which improves the stability of heavy-duty vehicles during emergency braking

Distributed Model Predictive Control over Multiple Groups of Vehicles in Highway Intelligent Space for Large Scale System

The paper presents the three time warning distances for solving the large scale system of multiple groups of vehicles safety driving characteristics towards highway tunnel environment based on distributed model prediction control approach. Generally speaking, the system includes two parts. First, multiple vehicles are divided into multiple groups. Meanwhile, the distributed model predictive control approach is proposed to calculate the information framework of each group. Each group of optimization performance considers the local optimization and the neighboring subgroup of optimization characteristics, which could ensure the global optimization performance. Second, the three time warning distances are studied based on the basic principles used for highway intelligent space (HIS) and the information framework concept is proposed according to the multiple groups of vehicles. The math model is built to avoid the chain avoidance of vehicles. The results demonstrate that the proposed highway intelligent space method could effectively ensure driving safety of multiple groups of vehicles under the environment of fog, rain, or snow