Microscopic traffic simulators are powerful tools for transportation engineers and planners to analyze and predict the impact of microscopic adjustments on traffic patterns without disrupting real-world traffic. For example, it can help analyze how changing road shape like replacing an intersection with a roundabout affects traffic patterns [1], and develop traffic-aware autonomous driving policies that enhance overall traffic efficiency [2, 3]. However, creating a realistic simulator that can simultaneously replicate the microscopic response of human drivers to traffic conditions and the resulting long-term macroscopic statistics is a challenging task. In recent years, there have been significant efforts to develop realistic traffic simulators that accurately model human driving behavior. Traditional traffic simulators, such as SUMO [4], AIMSUN [5], and MITSIM [6], typically rely on heuristic car-following models like the Intelligent Driver Model (IDM) [7]. However, despite careful calibration of parameters, these simplified, rulebased models often fail to deliver accurate simulations [8] due to the complexity of real-world traffic environments.

Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such data from multiple sensors to jointly optimize the prediction and planning tasks remains largely unexplored. In this paper, we present FusionAD, to the best of our knowledge, the first unified framework that fuse the information from two most critical sensors, camera and LiDAR, goes beyond perception task. Concretely, we first build a transformer based multi-modality fusion network to effectively produce fusion based features. In constrast to camera-based end-to-end method UniAD, we then establish a fusion aided modality-aware prediction and status-aware planning modules, dubbed FMSPnP that take advantages of multi-modality features. We conduct extensive experiments on commonly used benchmark nuScenes dataset, our FusionAD achieves state-of-the-art performance and surpassing baselines on average 15% on perception tasks like detection and tracking, 10% on occupancy prediction accuracy, reducing prediction error from 0.708 to 0.389 in ADE score and reduces the collision rate from 0.31% to only 0.12%.

Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing works mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this paper, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real-world deployment, 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which shows our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS