☆ 4.6 Review

Review of Decision-Making and Planning Approaches in Automated Driving

IEEE ACCESS (2022)

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Article Engineering, Civil

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Szilard Aradi

Summary: Academic research in the field of autonomous vehicles has gained popularity in recent years, covering various topics such as sensor technologies, communication, safety, decision making, and control. Artificial Intelligence and Machine Learning methods have become integral parts of this research. Motion planning, with a focus on strategic decision-making, trajectory planning, and control, has also been studied. This article specifically explores Deep Reinforcement Learning (DRL) as a field within Machine Learning. The paper provides insights into hierarchical motion planning and the basics of DRL, including environment modeling, state representation, perception models, reward mechanisms, and neural network implementation. It also discusses vehicle models, simulation possibilities, and computational requirements. The paper surveys state-of-the-art solutions, categorized by different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, and driving in dense traffic. Lastly, it raises open questions and future challenges.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Engineering, Civil

Deep Reinforcement Learning for Autonomous Driving: A Survey

B. Ravi Kiran et al.

Summary: This paper summarizes deep reinforcement learning algorithms, provides a taxonomy of automated driving tasks, discusses key computational challenges in real world deployment of autonomous driving agents, and explores adjacent domains as well as the role of simulators in training agents.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Engineering, Civil

Distributed Maneuver Planning With Connected and Automated Vehicles for Boosting Traffic Efficiency

Nathan Goulet et al.

Summary: The paper proposes a distributed predictive control framework for connected and automated vehicles (CAVs) in mixed traffic, with a focus on coordination planning and distributed speed assignment. The framework was extensively evaluated in traffic micro-simulations at various CAV penetrations, showing improvements in traffic flow, energy use, and lane utilization compared to a baseline scenario with no CAVs.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Proceedings Paper Automation & Control Systems

How To Not Drive: Learning Driving Constraints from Demonstration

Kasra Rezaee et al.

Summary: This paper proposes a scheme to learn motion planning constraints from human driving trajectories. By integrating behavioral planning and motion planning, feasible and safe trajectories can be generated for autonomous driving. Learning driving constraints can be used as an add-on module to existing autonomous driving solutions.

2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) (2022)

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Article Robotics

Interaction-Aware Motion Prediction for Autonomous Driving: A Multiple Model Kalman Filtering Scheme

Vasileios Lefkopoulos et al.

Summary: This study addresses the prediction of vehicle motion around an autonomous car, focusing on improved motion planning without inter-vehicle communication. A filtering scheme and an optimization-based projection are used for single-vehicle estimation and non-colliding predictions. The approach is extended to estimate multiple vehicles simultaneously with a dynamically adapted priority list.

IEEE ROBOTICS AND AUTOMATION LETTERS (2021)

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Article Engineering, Multidisciplinary

A probabilistic model for discretionary lane change proposals in highway driving situations

Manuel Schmidt et al.

Summary: This study presents a novel probabilistic approach for generating discretionary lane change proposals in highway driving situations, based on the quantification of driving lane utility. A driving simulator study was conducted to optimize model parameters and demonstrate applicability on real test vehicle data.

FORSCHUNG IM INGENIEURWESEN-ENGINEERING RESEARCH (2021)

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Article Engineering, Civil

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Sampo Kuutti et al.

Summary: Deep learning methods have shown great promise in providing excellent performance for complex and non-linear control problems, as well as generalising previously learned rules to new scenarios. While there have been important advancements in using deep learning for vehicle control, there are still challenges to overcome, such as computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

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Article Chemistry, Analytical

A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Nikita Smirnov et al.

Summary: It is important for autonomous vehicles to display human-like behavior for better understanding by other road users. Designing algorithms that can analyze human decision-making processes is crucial, especially in urban scenarios, to ensure road safety.

SENSORS (2021)

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Article Engineering, Civil

Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach

Peng Hang et al.

Summary: This paper presents a human-like decision making framework for AVs considering the coexistence of human-driven vehicles and autonomous vehicles in the future. Different driving styles, social interaction characteristics, game theory, and model predictive control are applied for decision making in AVs. Testing scenarios of lane change show that game theoretic approaches can provide reasonable human-like decision making, with the Stackelberg game theory approach reducing the cost value by over 20% under normal driving style compared to the Nash equilibrium approach.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Planning for Safe Abortable Overtaking Maneuvers in Autonomous Driving

Jiyo Palatti et al.

Summary: This paper presents a method for behavior and trajectory planning for safe autonomous overtaking, which allows the overtaking to be aborted if safety is compromised. By using a finite state machine and a combination of safe and reachable sets, the method can generate intermediate reference targets and plan dynamically feasible and collision-free trajectories. Simulation experiments show that this method can handle multiple driving behaviors within a single framework.

2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles

Fei Ye et al.

Summary: The survey systematically summarizes current literature on applying reinforcement learning to autonomous vehicle motion planning and control. The traditional pipeline approach consists of hand-crafted modules, while the emerging end-to-end approach offers better performance but faces challenges with expert data and generalization. Deep RL algorithms in autonomous driving still face challenges, with future research directions aimed at addressing them.

2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Trajectory-Based Failure Prediction for Autonomous Driving

Christopher B. Kuhn et al.

Summary: This paper proposes a method to early predict failures in autonomous driving by analyzing the planned trajectories of the vehicle and using machine learning to detect patterns that indicate impending failures. The approach, trained with data from BMW Group's development vehicles, outperforms existing state-of-the-art failure prediction by over 3% in Receiver Operating Characteristic analysis. The method, making no assumptions on the underlying system, can also be applied to predict failures in other safety-critical areas of robotics.

2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) (2021)

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Article Operations Research & Management Science

A branch-and-bound approach for a Vehicle Routing Problem with Customer Costs

Franziska Theurich et al.

Summary: This study focuses on the scheduling of tamping actions in railway maintenance management, modeling it as a Vehicle Routing Problem and proposing a solution based on branch-and-bound method. Through the comparison and analysis of two branching strategies and different lower bounds, the performance of the branch-and-bound method is evaluated and compared with a commercial solver.

EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION (2021)

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Review Engineering, Civil

A Review of Motion Planning for Highway Autonomous Driving

Laurene Claussmann et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)

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Article Computer Science, Artificial Intelligence

Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections

Yonghwan Jeong et al.

IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS (2020)

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Proceedings Paper Automation & Control Systems

Safe Planning for Self-Driving Via Adaptive Constrained ILQR

Yanjun Pan et al.

2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (2020)

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Design and Implementation of the Optimization Algorithm in the Layout of Parking Lot Guidance

Zhendong Liu et al.

2020 16TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS 2020) (2020)

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Artificial intelligence applications in the development of autonomous vehicles: a survey

Yifang Ma et al.

IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2020)

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A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles

Ziran Wang et al.

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2020)

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Predictive Maneuver Planning for an Autonomous Vehicle in Public Highway Traffic

Qian Wang et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)

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Article Robotics

Human-Like Motion Planning Based on Game Theoretic Decision Making

Annemarie Turnwald et al.

INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS (2019)

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Article Multidisciplinary Sciences

Social behavior for autonomous vehicles

Wilko Schwarting et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

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Review Green & Sustainable Science & Technology

Negotiation and Decision-Making for a Pedestrian Roadway Crossing: A Literature Review

Roja Ezzati Amini et al.

SUSTAINABILITY (2019)

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Article Chemistry, Multidisciplinary

Anti-Congestion Route Planning Scheme Based on Dijkstra Algorithm for Automatic Valet Parking System

Luyang Yu et al.

APPLIED SCIENCES-BASEL (2019)

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Article Computer Science, Information Systems

Intelligent route planning system based on interval computing

Wojciech Chmiel et al.

MULTIMEDIA TOOLS AND APPLICATIONS (2019)

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Article Computer Science, Information Systems

Real-Time Motion Planning Approach for Automated Driving in Urban Environments

Antonio Artunedo et al.

IEEE ACCESS (2019)

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Proceedings Paper Engineering, Electrical & Electronic

Path Planning with Discrete Geometric Shape Patterns

Bruno Simon et al.

2019 IEEE INTERNATIONAL CONFERENCE OF VEHICULAR ELECTRONICS AND SAFETY (ICVES 19) (2019)

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Article Environmental Sciences

Autonomous Road Vehicles: Challenges for Urban Planning in European Cities

Nikolaos Gavanas

URBAN SCIENCE (2019)

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Article Computer Science, Information Systems

Dynamic Trajectory Planning and Tracking for Autonomous Vehicle With Obstacle Avoidance Based on Model Predictive Control

Shaosong Li et al.

IEEE ACCESS (2019)

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Review Automation & Control Systems

Control of connected and automated vehicles: State of the art and future challenges

Jacopo Guanetti et al.

ANNUAL REVIEWS IN CONTROL (2018)

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A branch-and-cut algorithm for the Time Window Assignment Vehicle Routing Problem

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COMPUTERS & OPERATIONS RESEARCH (2018)

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Simultaneous Trajectory Planning and Tracking Using an MPC Method for Cyber-Physical Systems: A Case Study of Obstacle Avoidance for an Intelligent Vehicle

Hongyan Guo et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)

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Making Bertha Cooperate-Team AnnieWAY's Entry to the 2016 Grand Cooperative Driving Challenge

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2018)

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A branch-price-and-cut algorithm for the vehicle routing problem with time windows and multiple deliverymen

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TOP (2018)

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Globally energy-optimal speed planning for road vehicles on a given route

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2018)

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Connectivity-based optimization of vehicle route and speed for improved fuel economy

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Hybrid Trajectory Planning for Autonomous Driving in Highly Constrained Environments

Yu Zhang et al.

IEEE ACCESS (2018)

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Automated Driving in Uncertain Environments: Planning With Interaction and Uncertain Maneuver Prediction

Constantin Hubmann et al.

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES (2018)

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Tracking and Behavior Reasoning of Moving Vehicles Based on Roadway Geometry Constraints

Kichun Jo et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2017)

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Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2017)

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Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Mahmood Rahmani et al.

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Time-Optimal Maneuver Planning in Automatic Parallel Parking Using a Simultaneous Dynamic Optimization Approach

Bai Li et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2016)

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David Gonzalez et al.

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Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications

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IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2016)

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Julia Nilsson et al.

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2016)

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