Autonomous Vehicle Overtaking in a Bidirectional Mixed-Traffic Setting

Tariq, Faizan M.; Suriyarachchi, Nilesh; Mavridis, Christos N.; Baras, John S.

doi:10.23919/acc53348.2022.9867432

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…Remark 1: For the multi-lane highway driving scenario, occlusion does not play a prominent role so we do not account for it in the existing formulation. However, the proposed framework can easily accommodate occlusion and measurement uncertainties since the receding horizon approach bases its decision on the most up-to-date information available at any given time, as demonstrated in [20].…”

Section: Observation Modelmentioning

confidence: 99%

“…Here, (20) initializes the target lane, ( 21) restricts the target lane at any planning step to the set of available lanes, (22) restricts the lane change between consecutive planning steps to the adjacent lanes, and ( 23) represents the augmented safety constraint. The implication ( =⇒ ) in ( 22) can easily be transformed into a linear constraint (see Appendix).…”

Section: B Speed and Lane Advisory Systemmentioning

confidence: 99%

“…3) Feasibility: By an argument similar to the one presented in [20], it is a relatively straightforward proof for recursive feasibility of the problem, i.e. the optimization problem will continue to stay feasible, if initially feasible, with the trivial solution being matching the speed of the leading vehicle and not changing lanes.…”

Section: B Speed and Lane Advisory Systemmentioning

confidence: 99%

See 2 more Smart Citations

SLAS: Speed and Lane Advisory System for Highway Navigation

Tariq

Isele

Baras

et al. 2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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This paper proposes a hierarchical autonomous vehicle navigation architecture, composed of a high-level speed and lane advisory system (SLAS) coupled with low-level trajectory generation and trajectory following modules. Specifically, we target a multi-lane highway driving scenario where an autonomous ego vehicle navigates in traffic. We propose a novel receding horizon mixed-integer optimization based method for SLAS with the objective to minimize travel time while accounting for passenger comfort. We further incorporate various modifications in the proposed approach to improve the overall computational efficiency and achieve real-time performance. We demonstrate the efficacy of the proposed approach in contrast to the existing methods, when applied in conjunction with state-of-the-art trajectory generation and trajectory following frameworks, in a CARLA simulation environment.

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Section: Observation Modelmentioning

confidence: 99%

Section: B Speed and Lane Advisory Systemmentioning

confidence: 99%

Section: B Speed and Lane Advisory Systemmentioning

confidence: 99%

See 1 more Smart Citation

SLAS: Speed and Lane Advisory System for Highway Navigation

Tariq

Isele

Baras

et al. 2022

2022 IEEE 61st Conference on Decision and Control (CDC)

Self Cite

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“…Learning-based methods [8] and [9] have also been proposed for overtaking trajectory generation with real time operation capability but often lack safety guarantees. While these methods have not been applied to incoming lane overtaking, our previous work [10] explored the use of a mixed-integer model predictive control (MI-MPC) strategy for bidirectional overtaking for a single autonomous agent.…”

Section: Literature Reviewmentioning

confidence: 99%

Cooperative Bidirectional Mixed-Traffic Overtaking

Tariq

Suriyarachchi

Mavridis

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Safe overtaking, especially in a bidirectional mixed-traffic setting, remains a key challenge for Connected Autonomous Vehicles (CAVs). The presence of human-driven vehicles (HDVs), behavior unpredictability, and blind spots resulting from sensor occlusion make this a challenging control problem. To overcome these difficulties, we propose a cooperative communication-based approach that utilizes the information shared between CAVs to reduce the effects of sensor occlusion while benefiting from the local velocity prediction based on past tracking data. Our control framework aims to perform overtaking maneuvers with the objective of maximizing velocity while prioritizing safety and passenger comfort. Our method is also capable of reactively adjusting its plan to dynamic changes in the environment. The performance of the proposed approach is verified using realistic traffic simulations.

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“…This interaction is evident in dense traffic scenarios where all goal-directed behavior relies on the cooperation of other drivers to achieve the desired goal. To predict the nearby vehicles' trajectories, AVs often rely on simple predictive models such as assuming constant speed for other vehicles [2], treating them as bounded disturbances [3], or approximating their trajectories using a set of known trajectories [4]. These models do not capture the inter-vehicle interactions in their predictions.…”

Section: Introductionmentioning

confidence: 99%

Interaction-Aware Trajectory Planning for Autonomous Vehicles with Analytic Integration of Neural Networks into Model Predictive Control

Gupta¹,

Isele²,

Lee³

et al. 2023

Preprint

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Autonomous vehicles (AVs) must share the driving space with other drivers and often employ conservative motion planning strategies to ensure safety. These conservative strategies can negatively impact AV's performance and significantly slow traffic throughput. Therefore, to avoid conservatism, we design an interaction-aware motion planner for the ego vehicle (AV) that interacts with surrounding vehicles to perform complex maneuvers in a locally optimal manner. Our planner uses a neural network-based interactive trajectory predictor and analytically integrates it with model predictive control (MPC). We solve the MPC optimization using the alternating direction method of multipliers (ADMM) and prove the algorithm's convergence. We provide an empirical study and compare our method with a baseline heuristic method.

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Autonomous Vehicle Overtaking in a Bidirectional Mixed-Traffic Setting

Cited by 9 publications

References 25 publications

SLAS: Speed and Lane Advisory System for Highway Navigation

SLAS: Speed and Lane Advisory System for Highway Navigation

Cooperative Bidirectional Mixed-Traffic Overtaking

Interaction-Aware Trajectory Planning for Autonomous Vehicles with Analytic Integration of Neural Networks into Model Predictive Control

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