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SLAM (Simultaneous Localization and Mapping) plays a key role in autonomous driving. Recently, 4D Radar has attracted widespread attention because it breaks through the limitations of 3D millimeter wave radar and can simultaneously detect the distance, velocity, horizontal azimuth and elevation azimuth of the target with high resolution. However, there are few studies on 4D Radar in SLAM. In this paper, RI-FGO, a 4D Radar-Inertial SLAM method based on Factor Graph Optimization, is proposed. The RANSAC (Random Sample Consensus) method is used to eliminate the dynamic obstacle points from a single scan, and the ego-motion velocity is estimated from the static point cloud. A 4D Radar velocity factor is constructed in GTSAM to receive the estimated velocity in a single scan as a measurement and directly integrated into the factor graph. The 4D Radar point clouds of consecutive frames are matched as the odometry factor.

For intelligent vehicles, a fast and accurate estimation of road slope is of great significance for many aspects, including the steering comfort, fuel economy, vehicle stability control, driving decision-making, etc. But the commonly used estimation methods nowadays usually demand additional sensors or complex dynamic models, causing increase in system complexity as well as decrease in accuracy. To solve these problems, this paper puts forward a real-time road slope estimation algorithm leveraging the relationship between pitch angle and road slope, which only requires low sensors cost and computational complexity. Firstly, a GNSS/INS fusion system is established to obtain the pitch angle with respect to the navigation frame, which couples the vehicle’s pitch angle in vehicle frame and road slope angle.

Harmony-with-traffic refers to the ability of autonomous vehicles to maximize the driving benefits such as comfort, efficiency, and energy consumption of themselves and the surrounding traffic during interactive driving under traffic rules. In the test of harmony-with-traffic, one or more background vehicles that can respond to the driving behavior of the vehicle under test are required. For this purpose, the functional requirements of car-following model for harmony-with-traffic evaluation are analyzed from the dimensions of test conditions, constraints, steady state and dynamic response. Based on them, an interactive car-following model (ICFM) is developed. In this model, the concept of equivalent distance is proposed to transfer lateral influence to longitudinal. The calculation methods of expected speed are designed according to the different car-following modes divided by interaction object, reaction distance and equivalent distance.

The research and development of data-driven highly automated driving system components such as trajectory prediction, motion planning, driving test scenario generation, and safety validation all require large amounts of naturalistic vehicle trajectory data. Therefore, a variety of data collection methods have emerged to meet the growing demand. Among these, camera-equipped drones are gaining more and more attention because of their obvious advantages. Specifically, compared to others, drones have a wider field of bird's eye view, which is less likely to be blocked, and they could collect more complete and natural vehicle trajectory data. Besides, they are not easily observed by traffic participants and ensure that the human driver behavior data collected is realistic and natural. In this paper, we present a complete vehicle trajectory data extraction framework based on aerial videos. It consists of three parts: 1) objects detection, 2) data association, and 3) data cleaning.

Visual sensors are widely used in autonomous vehicles (AVs) for object detection due to the advantages of abundant information and low-cost. But the performance of visual sensors is highly affected by low light conditions when AVs driving at nighttime and in the tunnel. The low light conditions decrease the image quality and the performance of object detection, and may cause safety of the intended functionality (SOTIF) problems. Therefore, to analyze the performance limitations of visual sensors in low light conditions, a controlled light experiment on a proving ground is designed. The influences of low light conditions on the two-stage algorithm and the single-stage algorithm are compared and analyzed quantificationally by constructing an evaluation index set from three aspects of missing detection, classification, and positioning accuracy.

Recent researches in autonomous driving mainly consider the uncertainty in perception and prediction modules for safety enhancement. However, obstacles which block the field-of-view (FOV) of sensors could generate blind areas and leaves environmental uncertainty a remaining challenge for autonomous vehicles. Current solutions mainly rely on passive obstacles avoidance in path planning instead of active perception to deal with unexplored high-risky areas. In view of the problem, this paper introduces the concept of information entropy, which quantifies uncertain information in the blind area, into the motion planning module of autonomous vehicles. Based on model predictive control (MPC) scheme, the proposed algorithm can plan collision-free trajectories while actively explore unknown areas to minimize environmental uncertainty. Simulation results under various challenging scenarios demonstrate the improvement in safety and comfort with the proposed perception-aware planning scheme.

In recent years, intelligent driving technology is being extensively studied. This paper proposes a path planning method for perpendicular parking based on vehicle kinematics model using MPC optimization, which aims to solve the perpendicular parking task. Firstly, in the case of any initial position and orientation of the vehicle, judging whether the vehicle can be parked at one step according to the location of the parking place and the width of the lane, and then calculating the starting position for parking, and use the Bezier curve to connect the initial position and the starting position. Secondly, reference parking path is calculated according to the collision constraints of the parking space. Finally, because the parking path based on the vehicle kinematics model is composed of circle arcs and straight lines, the curvature of the path is discontinuous. The reference parking path is optimized using Model Predictive Control (MPC).

A tractor-trailer vehicle (TTV) consists of an actuated tractor attached with several full trailers. Because of its nonlinear and noncompleted constraints, it is a challenging task to avoid collisions for path planner. In this paper, we propose an efficient method to plan an optimal trajectory for TTV to reach the destination without any collision. To deal with the complicated constraints, the trajectory planning problem is formulated as an optimal control problem uniformly, which can be solved by the interior point method. A novel incremental optimization solving algorithm (IOSA) is proposed to accelerate the optimization process, which makes the number of trailers and the size of obstacles increase asynchronously. Simulation experiments are carried out in two scenarios with static obstacles. Compared with other methods, the results show that the planning method with IOSA outperforms in the efficiency.

Anti-slip regulator (ASR) is one of the most important research focuses in the field of vehicle active safety. An ASR for a distributed drive electric vehicle (DDEV) driven by four in-wheel motors is proposed in this paper, where a tire-road friction coefficient estimator and a road slope estimator are included making the ASR adaptive to road changes. The tire-road friction coefficient estimator is adopted to estimate road condition using improved Burckhardt model, so the optimal reference slip ratio is selected according to the estimated road adhesion coefficient for the maximum driving efficiency and the realization of adaptive anti-slip regulation. At the same time, the road slope is estimated using recursive least square with forgetting factor and the longitudinal acceleration sensor information is calibrated by the road slope estimation for slope adaptive velocity estimation.

Tracking vehicle trajectories is essential for autonomous vehicles and advanced driver-assistance systems to understand traffic environment and evaluate collision risk. In order to reduce the position deviation and fluctuation of tracking on-road vehicle by millimeter-wave radar (MMWR), an interactive multi-model Kalman filter (IMM-KF) tracking algorithm including data association and track management is proposed. In general, it is difficult to model the target vehicle accurately due to lack of vehicle kinematics parameters, like wheel base, uncertainty of driving behavior and limitation of sensor’s field of view. To handle the uncertainty problem, an interacting multiple model (IMM) approach using Kalman filters is employed to estimate multitarget’s states. Then the compensation of radar ego motion is achieved, since the original measurement is under the radar polar coordinate system.

On the dual-motor electric vehicle, which is driven by two electric motors mounted on the front and rear axles respectively, longitudinal dynamic control and electro-dynamic braking can be achieved by controlling the torque of front and rear axle motors respectively. Suspension displacement is related to the wheel torque, thus the pitch of vehicle body can be influenced by changing the torque distribution ratio. The pitch of the body has a great influence on the vehicle comfort, which occurs mainly during acceleration and braking progress. Traditionally active suspension is adopted to control the pitch of body. Instead, in this paper an ideal torque distribution strategy is developed to limit the pitch during acceleration and braking progress. This paper first explores the relationship between the torque distribution and the body pitch through the real vehicle test, which reveals the feasibility of the vehicle comfort promotion by optimizing the torque distribution coefficient.

A rapid path-planning algorithm that generates drivable paths for an autonomous vehicle operating in structural road is proposed in this paper. Cubic B-spline curve is adopted to generating smooth path for continuous curvature and, more, parametric basic points of the spline is adjusted to controlling the curvature extremum for kinematic constraints on vehicle. Other than previous approaches such as inverse kinematics, model-based prediction postprocess approach or closed-loop forward simulation, using the kinematics model in each iteration of path for smoothing and controlling curvature leading to time consumption increasing, our method characterized the vehicle curvature constraint by the minimum length of segment line, which synchronously realized constraint and smooth for generating path. And Differ from the path of robot escaping from a maze, the intelligent vehicle traveling on road in structured environments needs to meet the traffic rules.

Velocity is one of the most important inputs of active safety systems such as ABS, TCS, ESC, ACC, AEB et al. In a distributed drive electric vehicle equipped with four in-wheel motors, velocity is hard to obtain due to all-wheel drive, especially in wheel slipping conditions. This paper focus on longitudinal velocity estimation of the distributed drive electric vehicle. Firstly, a basic longitudinal velocity estimation method is built based on a typical Kalman filter, where four wheel speeds obtained by wheel speed sensors constitute an observation variable and the longitudinal acceleration measured by an inertia moment unit is chosen as input variable. In simulations, the typical Kalman filter show good results when no wheel slips; when one or more wheels slip, the typical Kalman filter with constant covariance matrices does not work well. Therefore, a gain matrix adjusting Kalman filter which can detect the wheel slip and cope with that is proposed.

This paper presents several pressure estimation algorithms (PEAs) for a decoupled electro-hydraulic brake system (EHB), which is driven by an electric motor + reduction gear. Most of the pressure control solutions are based on standard pressure-based feedback control, requiring a pressure signal. Although the pressure sensor can produce the pressure feedback signal, it will increase cost and enlarge installation space. The rotation angle of electric motor is available by the built-in sensor, so the pressure can be estimated by using the rotation angle. Considering the typical nonlinearities (i.e. friction, pressure-position relationship) and uncertainties (i.e. disturbance caused by friction model), the estimation-oriented model is established. The LuGre model is selected to describe the friction, and the pressure-position relationship is fitted by a quadratic polynomial.

The integrated control system which combines the differential drive assisted steering (DDAS) and the direct yaw moment control (DYC) for the distributed drive electric vehicle (DDEV) is studied. A handling improvement algorithm for the normal cornering maneuvers is proposed based on motion tracking control. Considering the ideal assistant power character curves at different velocities, an open-loop DDAS control strategy is developed to respond the driver’s demand of steering wheel torque. The DYC strategy contains the steering angle feedforward and the yaw rate feedback. The steering angle feedforward control strategy is employed to improve yaw rate steady gain of vehicle. The maximum feedforward coefficients at different velocities are obtained from the constraint of the motor external characteristic, final feedforward coefficients are calculated according to the ideal assistant power character curve of the DDAS.

Steering of skid-steered vehicles without steering mechanism is realized by differential drive/brake torque generated from in-wheel motors at left and right sides. Compared to traditional Ackerman-steered vehicles, skid-steered vehicles consume much more energy while steering due to greater steering resistance. Torque allocation is critical to the distributed drive skid-steered vehicles, since it influences not only steering performance, but also energy efficiency. In this paper, the dynamic characteristics of six-wheeled skid-steered vehicles were analyzed, and a 2-DOF vehicle model was established, which is important for both motion tracking control and torque allocation. Furthermore, a hierarchical controller was proposed. Considering tire force characteristics and tire slip, the upper layer calculates the generalized force and desired yaw moment based on anti-windup PI (proportion-integral) control method.

In this paper, a speed tracking controller is designed for the All-terrain vehicles. The method of feedforward with state variable feedback based on conditional integrators is adopted by the proposed control algorithm. The feedforward is designed considering the influence of the road slope on the longitudinal dynamics, which makes the All-terrain vehicles satisfy the acceleration demand of the upper controller when it tracks the desired speed on the road with slope varying greatly. The road slope is estimated based on a combined kinematic and dynamic model. This method solves the problem that road slope estimation requires an accurate vehicle dynamic model and are susceptible to acceleration sensor bias. Based on the vehicle dynamic model and the nonlinear tire model, the method of conditional integration is used in the state variable feedback, which considers the saturation constraint of the actuator with the intention of preventing the divergent integral operation.

Two control strategies, safety preferred control and master cylinder oscillation control, were designed for anti-lock braking on a novel integrated-electro-hydraulic braking system (I-EHB) which has only four solenoid valves in its innovative hydraulic control unit (HCU) instead of eight in a traditional one. The main idea of safety preferred control is to reduce the hydraulic pressure provided by the motor in the master cylinder whenever a wheel tends to be locking even if some of the other wheels may need more braking torque. In contrast, regarding master cylinder oscillation control, a sinusoidal signal is given to the motor making the hydraulic pressure in the master cylinder oscillate in certain frequency and amplitude. Hardware-in-the-loop simulations were conducted to verify the effectiveness of the two control strategies mentioned above and to evaluate them.

An integrated-electro-hydraulic brake system (I-EHB) is presented to fulfill the requirements of active safety. Because I-EHB can control the brake pressure accurately and fast. Furthermore I-EHB is a decoupled system, so it could make the maximum regenerative braking while offers the same brake pedal feeling and also good for ADAS and unmanned driving application. Based on the analysis of current electrohydraulic brake systems, regulation requirements and the requirements for brake system, the operating mode requirements of I-EHB are formed. Furthermore, system topological structure and a conceptual design are proposed. After the selection of key components, the parameter design is accomplished by modeling the system. According to the above-mentioned design method, an I-EHB prototype and test rig is made. Through the test rig, characteristics of the system are tested. Results show that this I-EHB system responded rapidly.

For an electric vehicle driven by four in-wheel motors, the torque of each wheel can be controlled precisely and independently. A closed-loop control method of differential drive assisted steering (DDAS) has been proposed to improve vehicle steering properties based on those advantages. With consideration of acceleration requirement, a three dimensional characteristic curve that indicates the relation between torque and angle of the steering wheel at different vehicle speeds was designed as a basis of the control system. In order to deal with the saturation of motor's output torque under certain conditions, an anti-windup PI control algorithm was designed. Simulations and vehicle tests, including pivot steering test, lemniscate test and central steering test were carried out to verify the performance of the DDAS in steering portability and road feeling.