Reinforcement Learning (RL) has achieved tremendous success in many complex decision-making tasks. However, safety concerns are raised during deploying RL in real-world applications, leading to a growing demand for safe RL algorithms, such as in autonomous driving and robotics scenarios. While safe control has a long history, the study of safe RL algorithms is still in the early stages. To establish a good foundation for future safe RL research, in this paper, we provide a review of safe RL from the perspectives of methods, theories, and applications. First, we review the progress of safe RL from five dimensions and come up with five crucial problems for safe RL being deployed in real-world applications, coined as “2H3W” . Second, we analyze the algorithm and theory progress from the perspectives of answering the “2H3W” problems. Particularly, the sample complexity of safe RL algorithms is reviewed and discussed, followed by an introduction to the applications and benchmarks of safe RL algorithms. Finally, we open the discussion of the challenging problems in safe RL, hoping to inspire future research on this thread. To advance the study of safe RL algorithms, we release an open-sourced repository containing major safe RL algorithms at the link.

As a bio-inspired and emerging sensor, an event-based neuromorphic vision sensor has a different working principle compared to the standard frame-based cameras, which leads to promising properties of low energy consumption, low latency, high dynamic range (HDR), and high temporal resolution. It poses a paradigm shift to sense and perceive the environment by capturing local pixel-level light intensity changes and producing asynchronous event streams. Advanced technologies for the visual sensing system of autonomous vehicles from standard computer vision to event-based neuromorphic vision have been developed. In this tutorial-like article, a comprehensive review of the emerging technology is given. First, the course of the development of the neuromorphic vision sensor that is derived from the understanding of biological retina is introduced. The signal processing techniques for event noise processing and event data representation are then discussed. Next, the signal processing algorithms and applications for event-based neuromorphic vision in autonomous driving and various assistance systems are reviewed. Finally, challenges and future research directions are pointed out. It is expected that this article will serve as a starting point for new researchers and engineers in the autonomous driving field and provide a bird's-eye view to both neuromorphic vision and autonomous driving research communities.

Although great progress has been made in generic object detection by advanced deep learning techniques, detecting small objects from images is still a difficult and challenging problem in the field of computer vision due to the limited size, less appearance, and geometry cues, and the lack of large-scale datasets of small targets. Improving the performance of small object detection has a wider significance in many real-world applications, such as self-driving cars, unmanned aerial vehicles, and robotics. In this article, the first-ever survey of recent studies in deep learning-based small object detection is presented. Our review begins with a brief introduction of the four pillars for small object detection, including multiscale representation, contextual information, super-resolution, and region-proposal . Then, the collection of state-of-the-art datasets for small object detection is listed. The performance of different methods on these datasets is reported later. Moreover, the state-of-the-art small object detection networks are investigated along with a special focus on the differences and modifications to improve the detection performance comparing to generic object detection architectures. Finally, several promising directions and tasks for future work in small object detection are provided. Researchers can track up-to-date studies on this webpage available at: https://github.com/tjtum-chenlab/SmallObjectDetectionList .

To enhance the active safety performance for automated electric vehicles (AEVs) at driving limits, the collaborative control of four-wheel steering (4WS) and direct yaw-moment control (DYC) is adopted. To deal with external disturbance and modeling error, Tube-based model predictive control (MPC) is applied to the control algorithm design, which takes the improvement of handling stability and path tracking performance into considerations. Taking the constraints into account, including control vector constraints, lateral stability constraints, rollover prevention constraints and path tracking error constraints, the integrated controller is designed and worked out by addressing the optimization issue. To verify the effectiveness and feasibility of the integrated controller, two extreme driving conditions are conducted based on hardware-in-the-loop (HIL) tests. Test results indicate that the integrated controller can improve vehicle’s handling stability and path tracking performance in unison at driving limits. Besides, the integrated controller shows strong robustness in the extreme conditions.

Neuromorphic Vision Datasets for Pedestrian Detection, Action Recognition, and Fall Detection

In the field of autonomous vehicles, various heterogeneous sensors, such as LiDAR, Radar, camera, etc, are combined to improve the vehicle ability of sensing accuracy and robustness. Multi-modal perception and learning has been proved to be an effective method to help vehicle understand the nature of complex environments. Event camera is a bio-inspired vision sensor that captures dynamic changes in the scene and filters out redundant information with high temporal resolution and high dynamic range. These characteristics of the event camera make it have a certain application potential in the field of autonomous vehicles. In this paper, we introduce a fully convolutional neural network with feature attention gate component (FAGC) for vehicle detection by combining frame-based and event-based vision. Both grayscale features and event features are fed into the feature attention gate component (FAGC) to generate the pixel-level attention feature coefficients to improve the feature discrimination ability of the network. Moreover, we explore the influence of different fusion strategies on the detection capability of the network. Experimental results demonstrate that our fusion method achieves the best detection accuracy and exceeds the accuracy of the method that only takes single-mode signal as input.