LCSim: A Large-Scale Controllable Traffic Simulator
Yuheng Zhang, Tianjian Ouyang, Fudan Yu, Lei Qiao, Wei Wu, Jingtao, Ding, Jian Yuan, Yong Li
TL;DR
LCSim is a large-scale, controllable traffic simulator that uses a unified data format and a diffusion-based motion planner to generate realistic, diverse, and customizable urban traffic scenarios for benchmarking autonomous driving algorithms.
Contribution
The paper introduces LCSim, a novel traffic simulator that combines a unified data format with a diffusion-based vehicle motion planner to enable large-scale, realistic, and controllable traffic scenario generation.
Findings
LCSim can generate diverse traffic scenarios reflecting various driving styles.
It supports large-scale simulation with realistic vehicle behaviors.
The simulator is suitable for benchmarking autonomous driving algorithms.
Abstract
With the rapid growth of urban transportation and the continuous progress in autonomous driving, a demand for robust benchmarking autonomous driving algorithms has emerged, calling for accurate modeling of large-scale urban traffic scenarios with diverse vehicle driving styles. Traditional traffic simulators, such as SUMO, often depend on hand-crafted scenarios and rule-based models, where vehicle actions are limited to speed adjustment and lane changes, making it difficult for them to create realistic traffic environments. In recent years, real-world traffic scenario datasets have been developed alongside advancements in autonomous driving, facilitating the rise of data-driven simulators and learning-based simulation methods. However, current data-driven simulators are often restricted to replicating the traffic scenarios and driving styles within the datasets they rely on, limiting…
Peer Reviews
Decision·ICLR 2025 Conference Withdrawn Submission
- The integration of a diffusion-based motion planner allows for more realistic and diverse vehicle behaviors compared to traditional rule-based models. - The unified data format for scenario construction from multiple sources (including Waymo, Argoverse, and OpenStreetMap) is a contribution, potentially enabling more comprehensive and varied simulation environments. - The authors' implementation of guide functions for controlling vehicle behaviors adds a layer of flexibility that could be val
My main concern lies in some overclaims and potential misrepresentations. 1. Firstly, the authors repeatedly refer to LCSim as a "simulation system" in both the title and contributions. However, a true traffic simulator should support "*closed-loop*" operations where vehicles run according to generated trajectories and continuously replan as the scenario changes. Despite the authors' claims, neither the manuscript nor the anonymous website demonstrates actual simulation effects, only showing o
* Simulator that supports several data formats: The authors developed a simulator based on a unified data format that supports several datasets that are otherwise not directly compatible to each other. * Diffusion model beats baselines: The diffusion motion planner model beats TrafficSim and SimNet baselines. * Dataset comparison: An interesting comparison between behaviors in WOMD and private datasets with differences in agent driving styles is provided. * Well written: The paper is generally w
* Lack of significant novelty: Various simulators for traffic simulation exist (as shown in Table 1). The paper argues to develop a new simulator with a unified data format. However, instead of unifying all simulators it risks developing yet another simulator (https://xkcd.com/927/). The diffusion-based motion policy also lacks significant novelty and is similar to CTG. * Single-threaded, slow simulator: Compared to Waymax (thank you for providing this comparison!), LCSim is slow and not acceler
- The diffusion-based motion planner for agents trained on WOMD provides a scalable and flexible way to model future actions of the agents - The guided loss function provides a stable method for driving behavior of the agents - The results provided are pretty comprehensive
- Three contributions are mentioned and I am not sure all of these are novel - First contribution is a unified data format for traffic scenarios. The authors do not mention OpenScenario which has been used as an unified data format. Also the third contribution is just mentioning the benchmarking the authors did for the paper, which does not count as a novel contribution. - The purpose of creating an unified data format is vague and the different sources were not used in the later motion plannin
**Impact** Traffic simulation is important for the self-driving industry. Though real-world data helps improve the realism of background agents, none of the existing simulated agents can be controlled to show specific behaviors, driving styles, or even out-of-distribution ones that don't exist in the training data, i.e. collision. The use of diffusion in recent years shows the promise to solve this problem. Though using diffusion to generate traffic agents is not new, this work is the first one
1. More qualitative results can be provided, especially the adversarial attack ones. It is tricky to generate reasonable adversarial behaviors rather than stupid rear collisions which is unavoidable for the ego car. 2. The conclusion of Table 3 seems to have Incorrect references, i.e. the third one is "with adv" not "with gentle". 3. What is the decision interval or simulation frequency? Would the closed-loop simulation performance change accordingly if the decision interval is changed? It seem
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Taxonomy
TopicsSimulation Techniques and Applications · Vehicular Ad Hoc Networks (VANETs) · Traffic Prediction and Management Techniques