Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.

Aiming at the problem of improving driving comfort under the condition of ensuring driving safety and limited suspension travel, this paper proposes a sliding mode adaptive controller suitable for strong disturbance action. Considering the nonlinear characteristics of the hydraulic actuator, a one-half vehicle suspension model with uncertain parameters and external disturbances is established. In the sliding mode design part of the controller, the sliding mode equivalent control items of the vertical and pitch subsystems are derived, and the optimised constant velocity approach law is adopted. The sliding mode switching control item is given to ensure that the controller can effectively adjust the vehicle body posture when the system is subjected to strong external disturbance. Finally, by selecting the adaptive law, the controller can adapt to the uncertainty of the vehicle body parameters. The simulation results for the specific example considered in this work show that the control strategy can greatly improve the ride comfort under the premise of ensuring the safety of the vehicle and the stability of the steering. Compared with the backstepping adaptive control, the control strategy of this paper shows superiority when the system is subjected to strong disturbance.