Recently, several 4D radar datasets have been published due to its high resolution and robustness in extreme weather conditions. However, most of the recent 4D radar dataset publications have insufficient odometry sensors, which is disadvantageous for 4D radar odometry research. To tackle this problem, this letter presents a 4D radar dataset with various odometry sensors based on a robot operating system (ROS) framework called MSC-RAD4R , which stands for Motivated for SLAM in City, ROS-based Automotive Dataset with 4D Radar . Our dataset at a glance includes 98,786 pairs of stereo images, 60,562 frames of LiDAR data, 90,864 frames of 4D radar data, 60,570 frames of RTK-GPS, 60,559 frames of GPS, 1,211,486 frames of IMU data and 6,057,276 wheel data covering approximately 51.6 km and 100 minutes of automotive data in various environments including day, night, snow and smoke. In particular, our setup includes a high-resolution 79 GHz Adaptive PDM FMCW Oculii 4D radar, which provides approximately 5,000–20,000 points per frame and operates at a long range of 400 meters with a frequency of 15 Hz. It is expected that the proposed dataset will be useful for researchers working on 4D radar SLAM.

Various torque distribution and stability control algorithms have been studied along with the development of four-wheel drive vehicles. But, most of those algorithms focus only on performance improvement. Since the independent four-wheel drive system is based on four drive motors, faults can occur in a motor or an inverter. If a fault occurs, the vehicle stability is not guaranteed and a fatal accident can occur. In this study, the fault-tolerant stability control algorithm for remaining healthy motors is proposed. This fault-tolerant control (FTC) system contains torque distribution and stability control algorithms. For accurate control of motor allocation, driving conditions such as under/over-steering, and steering direction should be considered, as well as vehicle condition such as fault motor location and motor performance limits. The proposed FTC is evaluated with two fault conditions (e.g., front motor fault, rear motor fault) under straight acceleration and constant steering acceleration scenarios. The behavior characteristics and driving risks caused by failures of motors while driving are analyzed. Also, the FTC algorithm is accurately follow a desired path within a minimum error range that the driver can cover.

This paper proposes a follower-stabilizing strategy for leader-waypoint-follower nonholonomic vehicle formation control. The follower-stabilizing strategy consists of the follower-stabilizing area and the follower's velocity correction term. When the follower crosses the waypoint generated by the leader, its steering is susceptible to oscillation. To overcome this issue, the follower-stabilizing area around the waypoint is proposed such that the follower imitates the leader's heading angle as it enters the follower-stabilizing area. At the same time, the velocity correction term drives the follower to track the waypoint stably. The follower-stabilizing strategy is validated by various simulations, which confirm that the follower-stabilizing area effectively reduces steering oscillation and that the velocity correction term verifies stable waypoint tracking. Additionally, GPS and AHRS-based small-scale vehicles are built to demonstrate the proposed method by experiment. The CTRA model-based Kalman filter is designed for GPS and AHRS to estimate the agents' states, such as the heading angle and linear velocities.

This paper focuses on the real-time obstacle avoidance and safe navigation of autonomous ground vehicles (AGVs). It introduces the Selective MPC-PF-PSO algorithm, which includes model predictive control (MPC), Artificial Potential Fields (APFs), and particle swarm optimization (PSO). This approach involves defining multiple sets of coefficients for adaptability to the surrounding environment. The simulation results demonstrate that the algorithm is appropriate for generating obstacle avoidance paths. The algorithm was implemented on the ROS platform using NVIDIA’s Jetson Xavier, and driving experiments were conducted with a steer-type AGV. Through measurements of computation time and real obstacle avoidance experiments, it was shown to be practical in the real world.

In this paper, a novel sensor system is presented for estimating the velocity using a modulated motion blur. By moving a camera mounted on the vehicle body with a specific pattern when the vehicle is moving, the blurred image includes the information of the vehicle velocity of the camera itself. It will be shown that the inclinations of motion blur in a scene are directly related to the velocity vector of the vehicle and the modulation speed. The proposed approach invariant to the exposure time provides the magnitude and direction of the velocity vector with high accuracy and high reliability. In contrast to other approaches using a camera image, our approach requires only 256 × 192 [pixel], and the proposed algorithm is simple and fast. The efficacy of the proposed method is demonstrated through simulations and experiments. The experimental results present empirical evidence to support that the proposed system is robust to climate changes such as rainy or snowy weather. The proposed system is expected to be applicable to vehicular technologies such as the vehicle dynamics controlling system or the vehicle positioning system.