Planning and Decision-Making for Autonomous Vehicles

By Wilko Schwarting, Javier Alonso-Mora, Daniela Rus. 2018

950 citations recorded by the source corpus.

In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and system-compliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, such as interactive planning and end-to-end learning, open up questions regarding safety and reliability that need to be addressed. In this survey, we emphasize recent approaches for integrated perception and planning and for behavior-aware planning, many of which rely on machine learning. This raises the question of verification and safety, which we also touch upon. Finally, we discuss the state of the art and remaining challenges for managing fleets of autonomous vehicles.

Subfields: Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Automotive Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, background): “A review on controllers, motion planning and learning based approaches for the same are provided in this review [15] for interested readers.”
A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “This enables DL planners to be integrated into conventional pipelines [28].”
Deep Imitative Models for Flexible Inference, Planning, and Control (2018, methodology): “…of spatial abstractions to plan to the goal location, common to model-based (not end-to-end) autonomous vehicle setups: coarse route planning over a road map, path planning within the observable space, and feedback control to follow the planned path (Paden et al., 2016; Schwarting et al., 2018).”
Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, neutral): “Navigating the dynamically changing, complex and diverse roads is a difﬁcult task for autonomous vehicles [164].”
Smart Transportation: An Overview of Technologies and Applications (2023, background): “These autonomous vehicles are equipped with hardware/sensors such as radar, lidar, and cameras to help them perceive their environment [94].”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “…approach does not consider the motion prediction of surrounding agents, which makes this type of method not robust to unknown situations [30], [350] The reactive approach determines the driving behavior based on the predicted motion of surrounding agents, which is also assuming the predicted…”
Exploring applications of deep reinforcement learning for real-world autonomous driving systems (2019, background): “A recent review on motion planning and control task can be found by authors (Schwarting et al., 2018).”
A survey of deep learning techniques for autonomous driving (2019, background): “, 2019) is to learn the behavior of a human driver from recorded driving experiences (Schwarting et al., 2018).”
A Survey of Deep Learning Applications to Autonomous Vehicle Control (2019, background): “in autonomous vehicle applications, including planning and decision making [27]–[31], perception [32]–[36], as well as mapping and localisation [37]–[39].”
Conditional Affordance Learning for Driving in Urban Environments (2018, background): “tion estimation [13,14]. This information can be aggregated into a model of the environment [15,16] and a planning module generates an obstacle-free trajectory which is executed by the control module [17]. The ﬁrst implementation of the imitation learning (IL) approach in the real world, ALVINN [18]”
Multimodal End-to-End Autonomous Driving (2019, background): “defined modules with particular responsibilities; for instance, to provide environmental perception [1], [2], as well as route planning and maneuver control [3], [4].”
Trust in AI: progress, challenges, and future directions (2024, positive): “…in many areas, including health (Itani et al., 2019; Shailaja et al., 2018; Wiens and Shenoy, 2018), autonomous transportation (Qayyum et al., 2020; Schwarting et al., 2018; Sligar, 2020), military (Galán et al., 2022; Roessingh et al., 2017), data security (Aljably et al., 2020; Pan et al.,…”