Review

Self-driving cars: A survey

Place recognition, which is also known as loop detection, has been one of the main problems in the field of robot navigation, and it is crucial in realizing high-performance simultaneous localization and mapping (SLAM). Most of the existing methods directly construct local or global descriptors with point clouds, ignoring the offset of different visits in the same place. To overcome the current limitations, this study emphasizes the importance of point cloud features in improving the environment recognition precision and proposes an innovative framework that combines local features that are spatially invariant and prominent descriptive global features as a representation of a laser scanner frame. The feature center of the laser point cloud is obtained by computing local feature points with spatial invariance, which improves the offset between the original point cloud and the feature center. Furthermore, a coarse-to-fine hierarchical recognition strategy is developed to improve the efficiency of place recognition. An evaluation with different offsets and synthesis is conducted on the publicly available KITTI and self-developed CHDloop datasets. The experimental results show that the proposed method can improve time efficiency by more than 18% compared to the SC and ISC, and there are also significant improvements in the recall rate and accuracy in slight lane-change scenarios. Moreover, the proposed method can obtain rich lane-change loops in moderate and large lane-change scenarios where the SC and ISC fail to obtain effective loops. The proposed method can be used as a lightweight and interpretable approach for place recognition, which not only can be quickly deployed in any SLAM framework but can also efficiently handle more complex place recognition issues in urban environments.

The contribution of this paper consists of a deep reinforcement learning (DRL) based method for autonomous train collision avoidance. While DRL applied to autonomous vehicles’ collision avoidance has shown interesting results compared to traditional methods, train-like vehicles are not currently covered. In addition, DRL applied to collision avoidance suffers from sparse rewards, which can lead to poor convergence and long training time. To overcome these limitations, this paper proposes a method for training a reinforcement learning (RL) agent for collision avoidance using local obstacle information mapped into occupancy grids. This method also integrates a network architecture containing a predictive auxiliary task consisting in future state prediction and encouraging the intermediate representation to be predictive of obstacle trajectories. A comparison study conducted on multiple simulated scenarios demonstrates that the trained policy outperforms other deep-learning-based policies as well as human driving in terms of both safety and efficiency. As a first step toward the certification of a DRL based method, this paper proposes to approximate the policy learned by the RL agent with an interpretable decision tree. Although this approximation results in a loss of performance, it enables a safety analysis of the learned function and thus paves the way to use the strengths of RL in certifiable algorithms. As this work is pioneering the use of RL for collision avoidance of rail-guided vehicles, and to facilitate future work by other engineers and researchers, a RL-ready simulator is provided with this paper.

The advent of virtual driving instructors has the potential to revolutionize driver education by providing real-time, unbiased feedback to learner drivers. This paradigm shift aims to mitigate the innate subjectivity associated with human evaluations. Our research focused on the creation of a virtual driving instructor capable of assessing a learner driver’s performance in real-time, with an emphasis on eliminating the inherent biases associated with human evaluations. Our approach involved the development of a rule-based assessment system, employing a multi-agent system based on the subsumption architecture. Each agent in the system was tasked with assessing a specific aspect of driving performance. Additionally, we utilized a knowledge graph to maintain a continuous understanding of the situational context, further enhancing the system’s assessment capabilities. We posited that our system, given its methodical structure and objective rule-based framework, would be able to accurately and objectively assess various driving scenarios. Further, we hypothesized that our system’s performance would be on par with expert human evaluations. The validation of our system was conducted using real driving sessions in simulators with actual students. The system was tested on various scenarios including intersections, roundabouts, and overtakes. The assessment results aligned closely with expert consensus, showcasing the system’s capacity to match the evaluative precision of human experts.

Trajectory forecasting is crucial for the advancement of autonomous vehicles. While much progress has been made, extant approaches often fall short in accounting for intricate social interactions and the unpredictability of human behavior. This paper introduces a novel multi-modal trajectory forecasting model named Multi-scale Interaction and Multi-pseudo-target Supervision (MIMS). Central to our approach is a multiscale hypergraph that discerns latent interactions across all scales, evaluating both their strength and type. Further enhancing our model’s capabilities is a causal-effect-estimation-based pseudo-target generation method. This facilitates multi-modality modeling in motion forecasting by offering explicit supervision with multiple latent targets. We validate MIMS using the Argoverse Motion Forecasting dataset. The results reveal that MIMS outperforms current state-of-the-art models across various traffic conditions. Specifically, our multiscale hypergraph exhibits a superior capacity for capturing complex spatiotemporal dependencies compared to pairwise methods. Moreover, our multi-pseudo-target supervision eliminates the pattern convergence of multi-modal forecasting.

State-of-the-art dense stereo matching algorithms have achieved excellent performance, demonstrating a capability to attain precise matching in most areas. However, current such methods rarely achieve this when images are captured under poor conditions. To improve the accuracy of the algorithm in such cases, this paper introduces a post-optimization algorithm to rectify matching errors and enhance outcomes. The main research areas of this paper include three aspects. (1) Disparities are classified into reliable and unreliable results based on the analysis of geometric matching relationships, local features in the images, and components within the matching cost function; (2) Subsequent analysis of horizontal image features identifies local characteristic indices calculated through integration along the horizontal axis, which establish specific matching criteria, forming the foundation for a cost volume that encompasses these distinct matches; (3) A redefined matching cost function is applied to previously classified unreliable results to rectify matching errors. This energy function is based on the cost volume above. Experimental results validate the efficacy of the proposed post-optimization algorithm, reducing the average matching errors from 8.66% to 5.85%.

Researching infrared adversarial attacks is crucial for ensuring the safe deployment of security-sensitive systems reliant on infrared object detectors. However, current research on infrared adversarial attacks mainly focuses on pedestrian detection tasks. Due to the complex shape and structure of vehicles and the changing working conditions, adversarial attack in infrared vehicle detection pose challenges like difficult multi-angle attack, poor physical transferability, and weak environmental adaptation. This paper proposed Leaf-like Mask Bar Code (LMBC), a novel adversarial attack method for multi-angle physical black-box attack on infrared detectors. Inspired by natural leaf structures, a mask was designed to restrict the adversarial patch contour. Then, adversarial parameters of the patches (angle, sparsity, and position) were optimized using the Genetic Algorithm with Multi-segment (GAM). Moreover, leaf-like structures in physical adversarial patches were constructed using suitable infrared coating materials. deploying them at multiple angles. Experimental results demonstrated LMBC’s efficacy, paralyzing the infrared vehicle detector with an Average Precision (AP) as low as 33.7% and an average Attack Success Rate (ASR) as high as 92.9% across a distance of 2.4m 4.2 m and angles of 0° 360°. Moreover, LMBC’s adversarial patches transferred to mainstream detectors (e.g., Faster RCNN, Yolov3, etc.) and pedestrian detection tasks.