8 research outputs found
TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models
With the promotion of chatgpt to the public, Large language models indeed
showcase remarkable common sense, reasoning, and planning skills, frequently
providing insightful guidance. These capabilities hold significant promise for
their application in urban traffic management and control. However, LLMs
struggle with addressing traffic issues, especially processing numerical data
and interacting with simulations, limiting their potential in solving
traffic-related challenges. In parallel, specialized traffic foundation models
exist but are typically designed for specific tasks with limited input-output
interactions. Combining these models with LLMs presents an opportunity to
enhance their capacity for tackling complex traffic-related problems and
providing insightful suggestions. To bridge this gap, we present TrafficGPT, a
fusion of ChatGPT and traffic foundation models. This integration yields the
following key enhancements: 1) empowering ChatGPT with the capacity to view,
analyze, process traffic data, and provide insightful decision support for
urban transportation system management; 2) facilitating the intelligent
deconstruction of broad and complex tasks and sequential utilization of traffic
foundation models for their gradual completion; 3) aiding human decision-making
in traffic control through natural language dialogues; and 4) enabling
interactive feedback and solicitation of revised outcomes. By seamlessly
intertwining large language model and traffic expertise, TrafficGPT not only
advances traffic management but also offers a novel approach to leveraging AI
capabilities in this domain. The TrafficGPT demo can be found in
https://github.com/lijlansg/TrafficGPT.git
Bringing Diversity to Autonomous Vehicles: An Interpretable Multi-vehicle Decision-making and Planning Framework
With the development of autonomous driving, it is becoming increasingly
common for autonomous vehicles (AVs) and human-driven vehicles (HVs) to travel
on the same roads. Existing single-vehicle planning algorithms on board
struggle to handle sophisticated social interactions in the real world.
Decisions made by these methods are difficult to understand for humans, raising
the risk of crashes and making them unlikely to be applied in practice.
Moreover, vehicle flows produced by open-source traffic simulators suffer from
being overly conservative and lacking behavioral diversity. We propose a
hierarchical multi-vehicle decision-making and planning framework with several
advantages. The framework jointly makes decisions for all vehicles within the
flow and reacts promptly to the dynamic environment through a high-frequency
planning module. The decision module produces interpretable action sequences
that can explicitly communicate self-intent to the surrounding HVs. We also
present the cooperation factor and trajectory weight set, bringing diversity to
autonomous vehicles in traffic at both the social and individual levels. The
superiority of our proposed framework is validated through experiments with
multiple scenarios, and the diverse behaviors in the generated vehicle
trajectories are demonstrated through closed-loop simulations
TrafficMCTS: A Closed-Loop Traffic Flow Generation Framework with Group-Based Monte Carlo Tree Search
Digital twins for intelligent transportation systems are currently attracting
great interests, in which generating realistic, diverse, and human-like traffic
flow in simulations is a formidable challenge. Current approaches often hinge
on predefined driver models, objective optimization, or reliance on
pre-recorded driving datasets, imposing limitations on their scalability,
versatility, and adaptability. In this paper, we introduce TrafficMCTS, an
innovative framework that harnesses the synergy of groupbased Monte Carlo tree
search (MCTS) and Social Value Orientation (SVO) to engender a multifaceted
traffic flow replete with varying driving styles and cooperative tendencies.
Anchored by a closed-loop architecture, our framework enables vehicles to
dynamically adapt to their environment in real time, and ensure feasible
collision-free trajectories. Through comprehensive comparisons with
state-of-the-art methods, we illuminate the advantages of our approach in terms
of computational efficiency, planning success rate, intent completion time, and
diversity metrics. Besides, we simulate highway and roundabout scenarios to
illustrate the effectiveness of the proposed framework and highlight its
ability to induce diverse social behaviors within the traffic flow. Finally, we
validate the scalability of TrafficMCTS by showcasing its prowess in
simultaneously mass vehicles within a sprawling road network, cultivating a
landscape of traffic flow that mirrors the intricacies of human behavior
LimSim: A Long-term Interactive Multi-scenario Traffic Simulator
With the growing popularity of digital twin and autonomous driving in
transportation, the demand for simulation systems capable of generating
high-fidelity and reliable scenarios is increasing. Existing simulation systems
suffer from a lack of support for different types of scenarios, and the vehicle
models used in these systems are too simplistic. Thus, such systems fail to
represent driving styles and multi-vehicle interactions, and struggle to handle
corner cases in the dataset. In this paper, we propose LimSim, the Long-term
Interactive Multi-scenario traffic Simulator, which aims to provide a long-term
continuous simulation capability under the urban road network. LimSim can
simulate fine-grained dynamic scenarios and focus on the diverse interactions
between multiple vehicles in the traffic flow. This paper provides a detailed
introduction to the framework and features of the LimSim, and demonstrates its
performance through case studies and experiments. LimSim is now open source on
GitHub: https://www.github.com/PJLab-ADG/LimSim .Comment: Accepted by 26th IEEE International Conference on Intelligent
Transportation Systems (ITSC 2023
Drive Like a Human: Rethinking Autonomous Driving with Large Language Models
In this paper, we explore the potential of using a large language model (LLM)
to understand the driving environment in a human-like manner and analyze its
ability to reason, interpret, and memorize when facing complex scenarios. We
argue that traditional optimization-based and modular autonomous driving (AD)
systems face inherent performance limitations when dealing with long-tail
corner cases. To address this problem, we propose that an ideal AD system
should drive like a human, accumulating experience through continuous driving
and using common sense to solve problems. To achieve this goal, we identify
three key abilities necessary for an AD system: reasoning, interpretation, and
memorization. We demonstrate the feasibility of employing an LLM in driving
scenarios by building a closed-loop system to showcase its comprehension and
environment-interaction abilities. Our extensive experiments show that the LLM
exhibits the impressive ability to reason and solve long-tailed cases,
providing valuable insights for the development of human-like autonomous
driving. The related code are available at
https://github.com/PJLab-ADG/DriveLikeAHuman
On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving
The pursuit of autonomous driving technology hinges on the sophisticated
integration of perception, decision-making, and control systems. Traditional
approaches, both data-driven and rule-based, have been hindered by their
inability to grasp the nuance of complex driving environments and the
intentions of other road users. This has been a significant bottleneck,
particularly in the development of common sense reasoning and nuanced scene
understanding necessary for safe and reliable autonomous driving. The advent of
Visual Language Models (VLM) represents a novel frontier in realizing fully
autonomous vehicle driving. This report provides an exhaustive evaluation of
the latest state-of-the-art VLM, GPT-4V(ision), and its application in
autonomous driving scenarios. We explore the model's abilities to understand
and reason about driving scenes, make decisions, and ultimately act in the
capacity of a driver. Our comprehensive tests span from basic scene recognition
to complex causal reasoning and real-time decision-making under varying
conditions. Our findings reveal that GPT-4V demonstrates superior performance
in scene understanding and causal reasoning compared to existing autonomous
systems. It showcases the potential to handle out-of-distribution scenarios,
recognize intentions, and make informed decisions in real driving contexts.
However, challenges remain, particularly in direction discernment, traffic
light recognition, vision grounding, and spatial reasoning tasks. These
limitations underscore the need for further research and development. Project
is now available on GitHub for interested parties to access and utilize:
\url{https://github.com/PJLab-ADG/GPT4V-AD-Exploration
Hybrid cooperative intersection management for connected automated vehicles and pedestrians
Connected Automated Vehicles (CAVs) have drawn much attention in recent years. High reliable automatic technologies can help CAVs to follow given trajectories well. However, safety and efficiency are hard to be ensured since the interactions between CAVs and pedestrians are complex problems. Thus, this study focuses on cooperative intersection management for CAVs and pedestrians. To avoid the effects of uncertainty about pedestrian behaviors, an indirect way is to use pedestrians’ signal lights to guide the movements of pedestrians, and such lights with communication devices can share information with CAVs to make decisions together. In time domains, a general conflict-free rule is established depending on the positions of CAVs and crosswalks. Geometric analysis with coordinate calculation is used to accurately determine the feasible vehicle trajectories and the reasonable periods for signal lights turning green. Four control strategies for the same conditions are compared in simulation experiments, and their performances are analyzed. We demonstrate that the proposed cooperative strategy not only balances the benefits of vehicles and pedestrians but also improves the traffic efficiency at the intersection