Testing autonomous driving algorithms on real autonomous vehicles is extremely costly and many researchers and developers in the field cannot afford a real car and the corresponding sensors. Although several free and open-source autonomous driving stacks, such as Autoware and Apollo are available, choices of open-source simulators to use with them are limited. In this paper, we introduce the LGSVL Simulator which is a high fidelity simulator for autonomous driving.The simulator engine provides end-to-end, full-stack simulation which is ready to be hooked up to Autoware and Apollo. In addition, simulator tools are provided with the core simulation engine which allow users to easily customize sensors, create new types of controllable objects, replace some modules in the core simulator, and create digital twins of particular environments.

We present a new approach to automated scenario-based testing of the safety of autonomous vehicles, especially those using advanced artificial intelligence-based components, spanning both simulation-based evaluation as well as testing in the real world. Our approach is based on formal methods, combining formal specification of scenarios and safety properties, algorithmic test case generation using formal simulation, test case selection for track testing, executing test cases on the track, and analyzing the resulting data. Experiments with a real autonomous vehicle at an industrial testing ground support our hypotheses that (i) formal simulation can be effective at identifying test cases to run on the track, and (ii) the gap between simulated and real worlds can be systematically evaluated and bridged.

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Self-attention mechanism, which has been successfully applied to current encoder-decoder framework of image captioning, is used to enhance the feature representation in the image encoder and capture the most relevant information for the language decoder. However, most existing methods will assign attention weights to all candidate vectors, which implicitly hypothesizes that all vectors are relevant. Moreover, current self-attention mechanisms ignore the intra-object attention distribution, and only consider the inter-object relationships. In this paper, we propose a Multi-Gate Attention (MGA) block, which expands the traditional self-attention by equipping with additional Attention Weight Gate (AWG) module and Self-Gated (SG) module. The former constrains the attention weights to be assigned to the most contributive objects. The latter is adopted to consider the intra-object attention distribution and eliminate the irrelevant information in object feature vector. Furthermore, most current image captioning methods apply the original transformer designed for natural language processing task, to refine image features directly. Therefore, we propose a pre-layernorm transformer to simplify the transformer architecture and make it more efficient for image feature enhancement. By integrating MGA block with pre-layernorm transformer architecture into the image encoder and AWG module into the language decoder, we present a novel Multi-Gate Attention Network (MGAN). The experiments on MS COCO dataset indicate that the MGAN outperforms most of the state-of-the-art, and further experiments on other methods combined with MGA blocks demonstrate the generalizability of our proposal. INDEX TERMS Image captioning, self-attention, transformer, multi-gate attention.

. Dense vehicle detection in rush hours is important for intelligent transportation systems. Most existing object detection methods can work well in off-peak vehicle detection for surveillance images. However, they may fail in dense vehicle detection in rush hours due to severe overlapping. To address this problem, a dense vehicle detection network is proposed by embedding the deformable channel-wise column transformer (DCCT) into the current you only look once (YOLO)-v5l network with a novel asymmetric focal loss (AF loss). The proposed DCCT fully extracts the column-wise occlusion information of vehicles in the images and guides the network to pay more attention to the visible area of partially occluded vehicles to improve the detection and positioning accuracy of weak feature targets. The proposed AF loss is used to balance the performance between easy and hard targets and address class imbalance. Extensive results demonstrate that the proposed network can accurately detect on-road densely located vehicles, even the minority classes in real time. Compared with the baseline YOLO-v5l, the mean average precision is improved by 3.93%, and it achieves comparable results with the existing state-of-the-art methods on the UA_Detrac dataset.