CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model

Decision-making and motion planning are pivotal in ensuring the safety and efficiency of Autonomous Vehicles (AVs). Existing methodologies typically adopt two paradigms: decision then planning or generation then scoring. However, the former often struggles with misalignment between decisions and planning, while the latter encounters significant challenges in integrating short-term operational utility with long-term tactical efficacy. To address these issues, we introduce 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. Comprehensive evaluations in nuPlan closed-loop simulation environments demonstrate the effectiveness of CALMM-Drive in achieving reliable and flexible driving performance, showcasing a significant advancement in the integration of uncertainty in LMM-empowered AVs. The code will be released upon acceptance.

Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles

This paper introduces a local planner that synergizes the decision making and trajectory planning modules towards autonomous driving. The decision making and trajectory planning tasks are jointly formulated as a nonlinear programming problem with an integrated objective function. However, integrating the discrete decision variables into the continuous trajectory optimization leads to a mixed-integer programming (MIP) problem with inherent nonlinearity and nonconvexity. To address the challenge in solving the problem, the original problem is decomposed into two sub-stages, and a two-stage optimization (TSO) based approach is presented to ensure the coherence in outcomes for the two stages. The optimization problem in the first stage determines the optimal decision sequence that acts as an informed initialization. With the outputs from the first stage, the second stage necessitates the use of a high-fidelity vehicle model and strict enforcement of the collision avoidance constraints as part of the trajectory planning problem. We evaluate the effectiveness of our proposed planner across diverse multi-lane scenarios. The results demonstrate that the proposed planner simultaneously generates a sequence of optimal decisions and the corresponding trajectory that significantly improves driving performance in terms of driving safety and traveling efficiency as compared to alternative methods. Additionally, we implement the closed-loop simulation in CARLA, and the results showcase the effectiveness of the proposed planner to adapt to changing driving situations with high computational efficiency.

LMMCoDrive: Cooperative Driving with Large Multimodal Model

To address the intricate challenges of decentralized cooperative scheduling and motion planning in Autonomous Mobility-on-Demand (AMoD) systems, this paper introduces LMMCoDrive, a novel cooperative driving framework that leverages a Large Multimodal Model (LMM) to enhance traffic efficiency in dynamic urban environments. This framework seamlessly integrates scheduling and motion planning processes to ensure the effective operation of Cooperative Autonomous Vehicles (CAVs). The spatial relationship between CAVs and passenger requests is abstracted into a Bird's-Eye View (BEV) to fully exploit the potential of the LMM. Besides, trajectories are cautiously refined for each CAV while ensuring collision avoidance through safety constraints. A decentralized optimization strategy, facilitated by the Alternating Direction Method of Multipliers (ADMM) within the LMM framework, is proposed to drive the graph evolution of CAVs. Simulation results demonstrate the pivotal role and significant impact of LMM in optimizing CAV scheduling and enhancing decentralized cooperative optimization process for each vehicle. This marks a substantial stride towards achieving practical, efficient, and safe AMoD systems that are poised to revolutionize urban transportation. The code is available at https://github.com/henryhcliu/LMMCoDrive.

Enhance Planning with Physics-informed Safety Controllor for End-to-end Autonomous Driving

Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark.

Robot Navigation in Unknown and Cluttered Workspace with Dynamical System Modulation in Starshaped Roadmap

This paper presents a novel reactive motion planning framework for navigating robots in unknown and cluttered 2D workspace. Typical existing methods are developed by enforcing the robot staying in free regions represented by the locally extracted ellipse or polygon. Instead, we navigate the robot in free space with an alternate starshaped decomposition, which is calculated directly from real-time sensor data. Additionally, a roadmap is constructed incrementally to maintain the connectivity information of the starshaped regions. Compared to the roadmap built upon connected polygons or ellipses in the conventional approaches, the concave starshaped region is better suited to capture the natural distribution of sensor data, so that the perception information can be fully exploited for robot navigation. In this sense, conservative and myopic behaviors are avoided with the proposed approach, and intricate obstacle configurations can be suitably accommodated in unknown and cluttered environments. Then, we design a heuristic exploration algorithm on the roadmap to determine the frontier points of the starshaped regions, from which short-term goals are selected to attract the robot towards the goal configuration. It is noteworthy that, a recovery mechanism is developed on the roadmap that is triggered once a non-extendable short-term goal is reached. This mechanism renders it possible to deal with dead-end situations that can be typically encountered in unknown and cluttered environments. Furthermore, safe and smooth motion within the starshaped regions is generated by employing the Dynamical System Modulation (DSM) approach on the constructed roadmap. Through comprehensive evaluation in both simulations and real-world experiments, the proposed method outperforms the benchmark methods in terms of success rate and traveling time.

Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication

This paper investigates a cooperative motion planning problem for large-scale connected autonomous vehicles (CAVs) under limited communications, which addresses the challenges of high communication and computing resource requirements. Our proposed methodology incorporates a parallel optimization algorithm with improved consensus ADMM considering a more realistic locally connected topology network, and time complexity of O(N) is achieved by exploiting the sparsity in the dual update process. To further enhance the computational efficiency, we employ a lightweight evolution strategy for the dynamic connectivity graph of CAVs, and each sub-problem split from the consensus ADMM only requires managing a small group of CAVs. The proposed method implemented with the receding horizon scheme is validated thoroughly, and comparisons with existing numerical solvers and approaches demonstrate the efficiency of our proposed algorithm. Also, simulations on large-scale cooperative driving tasks involving 80 vehicles are performed in the high-fidelity CARLA simulator, which highlights the remarkable computational efficiency, scalability, and effectiveness of our proposed development. Demonstration videos are available at https://henryhcliu.github.io/icadmm_cmp_carla.

Geometry-Aware Safety-Critical Local Reactive Controller for Robot Navigation in Unknown and Cluttered Environments

This work proposes a safety-critical local reactive controller that enables the robot to navigate in unknown and cluttered environments. In particular, the trajectory tracking task is formulated as a constrained polynomial optimization problem. Then, safety constraints are imposed on the control variables invoking the notion of polynomial positivity certificates in conjunction with their Sum-of-Squares (SOS) approximation, thereby confining the robot motion inside the locally extracted convex free region. It is noteworthy that, in the process of devising the proposed safety constraints, the geometry of the robot can be approximated using any shape that can be characterized with a set of polynomial functions. The optimization problem is further convexified into a semidefinite program (SDP) leveraging truncated multi-sequences (tms) and moment relaxation, which favorably facilitates the effective use of off-the-shelf conic programming solvers, such that real-time performance is attainable. Various robot navigation tasks are investigated to demonstrate the effectiveness of the proposed approach in terms of safety and tracking performance.

Real-Time Parallel Trajectory Optimization with Spatiotemporal Safety Constraints for Autonomous Driving in Congested Traffic

Multi-modal behaviors exhibited by surrounding vehicles (SVs) can typically lead to traffic congestion and reduce the travel efficiency of autonomous vehicles (AVs) in dense traffic. This paper proposes a real-time parallel trajectory optimization method for the AV to achieve high travel efficiency in dynamic and congested environments. A spatiotemporal safety module is developed to facilitate the safe interaction between the AV and SVs in the presence of trajectory prediction errors resulting from the multi-modal behaviors of the SVs. By leveraging multiple shooting and constraint transcription, we transform the trajectory optimization problem into a nonlinear programming problem, which allows for the use of optimization solvers and parallel computing techniques to generate multiple feasible trajectories in parallel. Subsequently, these spatiotemporal trajectories are fed into a multi-objective evaluation module considering both safety and efficiency objectives, such that the optimal feasible trajectory corresponding to the optimal target lane can be selected. The proposed framework is validated through simulations in a dense and congested driving scenario with multiple uncertain SVs. The results demonstrate that our method enables the AV to safely navigate through a dense and congested traffic scenario while achieving high travel efficiency and task accuracy in real time.

Integrated Decision-Making and Control for Urban Autonomous Driving with Traffic Rules Compliance

In urban driving scenarios, autonomous vehicles are expected to conform to traffic rules covering traffic lights, traversable and non-traversable traffic lines, etc. In this article, we propose an optimization-based integrated decision-making and control scheme for urban autonomous driving. Inherently, to ensure the compliance with traffic rules, an innovative design of potential functions (PFs) is presented to characterize various traffic rules that are commonly encountered in urban driving scenarios, and these PFs are further incorporated as part of the model predictive control (MPC) formulation. In this sense, it circumvents the necessity of typical hand-crafted rule design, and high-level decision-making is attained implicitly along with control as an integrated architecture, facilitating flexible maneuvers with safety guarantees. As demonstrated from a series of simulations in CARLA, it is noteworthy that the proposed framework admits real-time performance and high generalizability.

Parallel Optimization for Cooperative Autonomous Driving at Unsignalized Roundabouts with Hard Safety Guarantees

The development of connected autonomous vehicles (CAVs) facilitates the enhancement of traffic efficiency in complicated scenarios. In unsignalized roundabout scenarios, difficulties remain unsolved in developing an effective and efficient coordination strategy for CAVs. In this paper, we formulate the cooperative autonomous driving problem of CAVs in the roundabout scenario as a constrained optimal control problem, and propose a computationally-efficient parallel optimization framework to generate strategies for CAVs such that the travel efficiency is improved with hard safety guarantees. All constraints involved in the roundabout scenario are addressed appropriately with convex approximation, such that the convexity property of the reformulated optimization problem is exhibited. Then, a parallel optimization algorithm is presented to solve the reformulated optimization problem, where an embodied iterative nearest neighbor search strategy to determine the optimal passing sequence in the roundabout scenario. It is noteworthy that the travel efficiency in the roundabout scenario is enhanced and the computation burden is considerably alleviated with the innovation development. We also examine the proposed method in CARLA simulator and perform thorough comparisons with a rule-based baseline and the commonly used IPOPT optimization solver to demonstrate the effectiveness and efficiency of the proposed approach.