Autonomous Driving
CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model
This study proposes CALMM-Drive, a novel Confidence-Aware Large Multimodal Model (LMM) empowered Autonomous Driving framework. Our approach employs Top-K confidence elicitation, which facilitates the generation of multiple candidate decisions along with their confidence levels. Furthermore, we propose a novel planning module that integrates a diffusion model for trajectory generation and a hierarchical refinement process to find the optimal path. This framework enables the selection of the best plan accounting for both low-level solution quality and high-level tactical confidence, which mitigates the risks of one-shot decisions and overcomes the limitations induced by short-sighted scoring mechanisms.
Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles
This research highlights the development of an end-to-end planning framework that enhances driving performance beyond mere imitation of expert demonstrations. By framing decision-making and trajectory planning as a differentiable nonlinear optimization problem, the framework effectively integrates with a learning-based approach while preserving intrinsic constraints throughout the learning process. Validated on the Waymo Open Motion dataset, it consistently outperforms baseline methods. We provide a thorough analysis of how optimized decisions contribute to overall enhancements in driving performance.
Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles
This paper presents a local planner that combines decision-making and trajectory planning for autonomous driving, structured as a nonlinear programming problem. To address the complexities of mixed-integer programming, a two-stage optimization (TSO) approach is proposed. Our closed-loop simulations in CARLA highlight its adaptability to changing driving conditions with high computational efficiency.
Game-Theoretic Driver Modeling and Decision-Making for Autonomous Driving with Temporal-Spatial Attention-Based Deep Q-Learning
A temporal-spatial attention-based deep Q-learning (TSA-DQN) algorithm is developed to estimate the decision level of surrounding vehicles and optimize ego vehicle's decision. Simulations demonstrate improved safety, efficiency, and success rates over baselines in various driving scenarios.
A Universal Multi-Vehicle Cooperative Decision-Making Approach in Structured Roads by Mixed-Integer Potential Game
This paper proposes a universal multi-vehicle cooperative decision-making method in structured roads with game theory. We transform the decision-making problem into a graph path searching problem within a way-point graph framework. The problem is formulated as a mixed-integer linear programming problem (MILP) first and transformed into a mixed-integer potential game (MIPG). Two Gauss-Seidel algorithms for cooperative decision-making are presented to solve the MIPG problem.
Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach
This research proposes an innovative integrated decision-making and trajectory planning framework for autonomous vehicles. The approach models multimodal interactions of traffic participants as a general Bayesian game, and the corresponding Bayesian coarse correlated equilibrium (Bayes-CCE) reveals the optimal decision and planning scheme under multimodal uncertainties.
LMMCoDrive: Cooperative Driving with Large Multimodal Model
This research introduces LMMCoDrive, a framework for decentralized cooperative scheduling and motion planning in Autonomous Mobility-on-Demand (AMoD) systems. It integrates scheduling and motion planning using a Large Multimodal Model (LMM) with BEV representation, refining CAV trajectories while ensuring safety. A decentralized ADMM-based optimization strategy evolves the CAV graph. Simulations highlight LMM's effectiveness in enhancing traffic efficiency and safe, practical AMoD operations.
Safe and Real-Time Consistent Planning for Autonomous Vehicles in Partially Observed Environments via Parallel Consensus Optimization
This research introduces a consistent parallel trajectory optimization (CPTO) approach for real-time, consistent, and safe trajectory planning for autonomous driving in partially observed environments. The CPTO framework introduces a consensus safety barrier module, ensuring that each generated trajectory maintains a consistent and safe segment, even when faced with varying levels of obstacle detection accuracy. We validate our CPTO framework through extensive comparisons with state-of-the-art baselines across multiple driving tasks in partially observable environments. Our results demonstrate improved safety and consistency using both synthetic and real-world traffic datasets.
Barrier-Enhanced Parallel Homotopic Trajectory Optimization for Safety-Critical Autonomous Driving
This research seamlessly integrates discrete decision-making maneuvers with continuous trajectory variables for safety-critical autonomous driving. The algorithm operates in real-time, optimizing trajectories of autonomous vehicles to ensure safety, stability, and proactive interaction with uncertain human-driven vehicles across various driving tasks, utilizing over-relaxed ADMM iterations. We provide a comprehensive theoretical analysis of safety and computational efficiency.
Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication
This research presents a parallel optimization algorithm for cooperative motion planning of large-scale CAVs under limited communications, achieving O(N) time complexity by leveraging sparsity and an improved consensus ADMM. A lightweight evolution strategy enhances computational efficiency, managing small CAV groups. The method, validated with a receding horizon scheme, outperforms existing solvers in simulations of up to 100 vehicles in CARLA, showcasing its efficiency, scalability, and effectiveness.
A Universal Cooperative Decision-Making Framework for Connected Autonomous Vehicles with Generic Road Topologies
This research proposes a general approach for optimal cooperative decision-making of connected autonomous vehicles (CAVs). The approach utilizes the graph representation of generic road topologies and reformulates the cooperative decision-making problem of CAVs as a mixed-integer linear program (MILP). The corresponding solution results in optimal cooperative decision-making, and simulations in various traffic scenarios demonstrate improved comfort, security, and traffic efficiency.
Spatiotemporal Receding Horizon Control with Proactive Interaction Towards Autonomous Driving in Dense Traffic
This research proposes a computationally-efficient spatiotemporal receding horizon control (ST-RHC) scheme to generate a safe, dynamically feasible, energy-efficient trajectory in control space, where different driving tasks in dense traffic can be achieved with high accuracy and safety in real time. The effectiveness of the proposed ST-RHC scheme is demonstrated through comprehensive comparisons with state-of-the-art algorithms on synthetic and real-world traffic datasets under dense traffic.
Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM
This research proposes a decentralized iterative LQR algorithm for cooperative trajectory planning of connected autonomous vehicles (CAVs) using dual consensus ADMM. The approach reformulates a non-convex problem into a series of convex ones, enabling parallel optimization. Real-time performance and scalability are achieved through efficient trajectory updates. Experiments show superior scalability and efficiency compared to baseline methods.
Robotics
Collision-Free Trajectory Optimization in Cluttered Environments Using Sums-of-Squares Programming
We propose FRTree, a novel navigation framework that leverages a dynamic treestructure of free regions for robot navigation in cluttered, unknown enyironments with narrow passages. FRTree continuously incorporates real-time perceptive information to identify distinct navigation options and dynamically expands the tree toward explorable and traversable directions. Extensive simulations and real-world tests show that FRTree outperforms benchmark methods in generating safe and efficient motion plans in highly cluttered and unknown terrains.
GS-LIVM: Real-Time Photo-Realistic LiDAR-Inertial-Visual Mapping with Gaussian Splatting
This research introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), our approach enables real-time photo-realistic mapping while ensuring high-quality image rendering in large-scale unbounded outdoor environments.
Arm-Constrained Curriculum Learning for Loco-Manipulation of a Wheel-Legged Robot
This research introduces an arm-constrained curriculum learning architecture to tackle the issues introduced by adding the manipulator. Firstly, we develop an arm-constrained reinforcement learning algorithm to ensure safety and reliability in control performance after equipping the manipulator. Additionally, to address discrepancies in reward settings between the arm and the base, we propose a reward-aware curriculum learning method. The policy is first trained in Isaac gym and transferred to the physical robot to complete grasping tasks, including the door-opening task, fan-twitching task and the relay-baton-picking and following task. The results demonstrate that our proposed approach effectively controls the arm-equipped wheel-legged robot to master grasping abilities including the dynamic grasping skills, allowing it to chase and catch a moving object while in motion. Please refer to our website for the code and supplemental videos.
Collision-Free Trajectory Optimization in Cluttered Environments Using Sums-of-Squares Programming
This research introduces a trajectory optimization framework for robot navigation in cluttered 3D environments by guiding robot motion through a graph of convex regions within collision-free space. A Sums-of-Squares (SOS) optimization problem is formulated to determine the minimum scaling factor for the region to contain the robot at fixed configuration, and safety constraints are then established by limiting the scaling factor along the trajectory. A guiding direction, derived from the Lagrangian gradient at the SOS optimum is integrated with the AL-iLQR algorithm to efficiently solve the nonlinear trajectory optimization problem.
Geometry-Aware Safety-Critical Local Reactive Controller for Robot Navigation in Unknown and Cluttered Environments
This research proposes a safety-critical local reactive controller for robot navigation in unknown environments. The trajectory tracking task is formulated as a constrained polynomial optimization problem with safety constraints imposed via Sum-of-Squares (SOS) certificates. The problem is convexified into a semidefinite program (SDP) using truncated multi-sequences and moment relaxation, enabling real-time performance.
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