Real-Time Constrained Trajectory Planning and Vehicle Control for Proactive Autonomous Driving With Road Users

Batkovic, Ivo; Zanon, Mario; Ali, Mohammad; Falcone, Paolo

doi:10.23919/ecc.2019.8796099

Cited by 48 publications

(32 citation statements)

References 22 publications

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“…In contrast to similar approaches like [3] that assume constant velocity for other vehicles future trajectory prediction or [13] which rely on external prediction modules, we consider the worst-case predictions for safety constraints inside MPC and provide anytime safe trajectories which are summarized here:…”

Section: A Safe Trajectory Planning With Model Predictive Controlmentioning

confidence: 99%

High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging

Kamran¹,

Yu²,

Lauer³

2021

Preprint

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Reinforcement learning (RL) has recently been used for solving challenging decision-making problems in the context of automated driving. However, one of the main drawbacks of the presented RL-based policies is the lack of safety guarantees, since they strive to reduce the expected number of collisions but still tolerate them. In this paper, we propose an efficient RL-based decision-making pipeline for safe and cooperative automated driving in merging scenarios. The RL agent is able to predict the current situation and provide high-level decisions, specifying the operation mode of the low level planner which is responsible for safety. In order to learn a more generic policy, we propose a scalable RL architecture for the merging scenario that is not sensitive to changes in the environment configurations. According to our experiments, the proposed RL agent can efficiently identify cooperative drivers from their vehicle state history and generate interactive maneuvers, resulting in faster and more comfortable automated driving. At the same time, thanks to the safety constraints inside the planner, all of the maneuvers are collision free and safe.

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Section: A Safe Trajectory Planning With Model Predictive Controlmentioning

confidence: 99%

High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging

Kamran¹,

Yu²,

Lauer³

2021

Preprint

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“…For example, to implement the path tracking at low speed, a simpler vehicle model is sufficient and provides reasonable accuracy [2]. A kinematic vehicle model is a popular choice for designing PTC for the lower-speed operation of AVs [3]- [5]. However, due to unmodelled dynamics, a kinematic vehicle model is not viable at higher speeds [2].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

et al. 2021

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Model Predictive Controller (MPC) is a capable technique for designing Path Tracking Controller (PTC) of Autonomous Vehicles (AVs). The performance of MPC can be significantly enhanced by adopting a high-fidelity and accurate vehicle model. This model should be capable of capturing the full dynamics of the vehicle, including nonlinearities and uncertainties, without imposing a high computational cost for MPC. A data-driven approach realised by learning vehicle dynamics using vehicle operation data can offer a promising solution by providing a suitable trade-off between accurate state predictions and the computational cost for MPC. This work proposes a framework for designing an MPC with a Neural Network (NN)-based learned dynamic model of the vehicle using the plethora of data available from modern vehicle systems. The objective is to integrate an NN-based model with higher accuracy than the conventional vehicle models for the required prediction horizon into MPC for improved tracking performances. The proposed NN-based model is highly capable of approximating latent system states, which are difficult to estimate, and provides more accurate predictions in the presence of parametric uncertainties. The observation of the results in various road conditions shows that the proposed approach outperforms the MPCs with conventional vehicle models.

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“…Fujinami et al [39] presented a speed control algorithm specialized for intersection environments based on an autonomous emergency braking system. Batkovic et al [40] also proposed a proactive vehicle motion planner to manage the collision risk caused by pedestrians.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Motion Prediction-Based Proactive Motion Planning for Autonomous Driving in Urban Environments

Jeong

2021

IEEE Access

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This paper presents self-adaptive motion prediction-based proactive motion planning for autonomous driving in urban environments. In order to achieve fully autonomous driving in urban environments, the proposed algorithm predicts future behavior of moving vehicles and considers the potential risk of objects appearing suddenly from occluded regions. A self-adaptive motion predictor was used to predict the probabilistic future states of vehicles and estimate the uncertainty of prediction simultaneously. Then, a free space boundary and drivable corridor were defined to determine the desired longitudinal acceleration and path. Based on these two boundary definitions, the proposed proactive motion planner decided the desired longitudinal motion by considering the risk potential of objects, such as the appearance of vulnerable road users from the free space boundary. The desired path is determined within the drivable corridor by the proposed motion planner with an integrated motion optimizer. The performance of the proposed algorithm has been validated via vehicle tests. The test results demonstrated that the proposed algorithm proactively determined lateral and longitudinal motion to minimize risks caused by detected and possible objects simultaneously on complex narrow roads to ensure safety.

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Real-Time Constrained Trajectory Planning and Vehicle Control for Proactive Autonomous Driving With Road Users

Cited by 48 publications

References 22 publications

High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging

High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging

Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

Self-Adaptive Motion Prediction-Based Proactive Motion Planning for Autonomous Driving in Urban Environments

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