Real-time deep reinforcement learning based vehicle navigation

Koh, Songsang; Zhou, Bo; Fang, Hui; Yang, Po; Yang, Zaili; Yang, Qiang; Guan, Lin; Ji, Zhigang

doi:10.1016/j.asoc.2020.106694

Cited by 78 publications

(42 citation statements)

References 33 publications

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“…Koh et al [11] presented a deep reinforcement learning method to enable real-time interaction between vehicles and complex urban environments. By defining tasks as a sequence of decisions, a real-time intelligent vehicle routing and navigation system is constructed.…”

Section: Reinforcement Learning In Routing Planningmentioning

confidence: 99%

Time-optimal and privacy preserving route planning for carpool policy

Zhu

et al. 2022

World Wide Web

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To alleviate the traffic congestion caused by the sharp increase in the number of private cars and save commuting costs, taxi carpooling service has become the choice of many people. Current research on taxi carpooling services has focused on shortening the detour distances. While with the development of intelligent cities, efficiently match passengers and vehicles and planning routes become urgent. And the privacy between passengers in the taxi carpooling service also needs to be considered. In this paper, we propose a time-optimal and privacy-preserving carpool route planning system via deep reinforcement learning. This system uses the traffic information around the carpooling vehicle to optimize passengers’ travel time, not only to efficiently match passengers and vehicles but also to generate detailed route planning for carpooling vehicles. We conducted experiments on an Internet of Vehicles simulator CARLA, and the results demonstrate that our method is better than other advanced methods and has better performance in complex environments.

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Section: Reinforcement Learning In Routing Planningmentioning

confidence: 99%

Time-optimal and privacy preserving route planning for carpool policy

Zhu

et al. 2022

World Wide Web

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“…The reinforcement learning paradigm fits naturally into the navigation tasks and has encouraged a wide range of practical studies. In this work we focus on vehicle navigation Koh et al (2020); Kiran et al (2021); Stafylopatis and Blekas (1998); Deshpande and Spalanzani (2019). Previous work can be separated into two major categories: navigation with human interaction Thomaz et al (2006); Wang et al (2003); Hemminahaus and Kopp (2017) and navigation without human interaction Kahn et al (2018); Ross et al (2008).…”

Section: Rl For Navigation Systemmentioning

confidence: 99%

“…We conduct extensive experiments (60+ hours) on real human participants with diverse driving experiences as shown in Fig. 1, which is significantly more challenging than most previous works that only perform experiments with simulated agents instead of real humans Koh et al (2020); Nagabandi et al (2018b). The user study shows that our algorithm outperforms the baselines in following optimal routes and it is better at enforcing safety where the collision rate is significantly reduced by more than 60%.…”

Section: Introductionmentioning

confidence: 99%

Online No-regret Model-Based Meta RL for Personalized Navigation

Song¹,

Yuan²,

Sun³

2022

Preprint

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The interaction between a vehicle navigation system and the driver of the vehicle can be formulated as a model-based reinforcement learning problem, where the navigation systems (agent) must quickly adapt to the characteristics of the driver (environmental dynamics) to provide the best sequence of turn-by-turn driving instructions. Most modern day navigation systems (e.g, Google maps, Waze, Garmin) are not designed to personalize their low-level interactions for individual users across a wide range of driving styles (e.g., vehicle type, reaction time, level of expertise). Towards the development of personalized navigation systems that adapt to a variety of driving styles, we propose an online no-regret model-based RL method that quickly conforms to the dynamics of the current user. As the user interacts with it, the navigation system quickly builds a user-specific model, from which navigation commands are optimized using model predictive control. By personalizing the policy in this way, our method is able to give well-timed driving instructions that match the user's dynamics. Our theoretical analysis shows that our method is a no-regret algorithm and we provide the convergence rate in the agnostic setting. Our empirical analysis with 60+ hours of real-world user data using a driving simulator shows that our method can reduce the number of collisions by more than 60%.

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“…[18] formulated the dynamic shortest path problem as a mixed-integer programming problem. [19] has used RL for real-time vehicle navigation and routing. However, both of these studies have tackled a general routing problem, and signal pre-emption and its influence on traffic have not been modeled.…”

Section: Supplementary Materials S1 Related Workmentioning

confidence: 99%

A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles

Su¹,

Zhong²,

Dey³

et al. 2021

Preprint

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Emergency vehicles (EMVs) play a critical role in a city's response to time-critical events such as medical emergencies and fire outbreaks. The existing approaches to reduce EMV travel time employ route optimization and traffic signal pre-emption without accounting for the coupling between route these two subproblems. As a result, the planned route often becomes suboptimal. In addition, these approaches also do not focus on minimizing disruption to the overall traffic flow. To address these issues, we introduce EMVLight in this paper. This is a decentralized reinforcement learning (RL) framework for simultaneous dynamic routing and traffic signal control. EMVLight extends Dijkstra's algorithm to efficiently update the optimal route for an EMV in real-time as it travels through the traffic network. Consequently, the decentralized RL agents learn network-level cooperative traffic signal phase strategies that reduce EMV travel time and the average travel time of non-EMVs in the network. We have carried out comprehensive experiments with synthetic and real-world maps to demonstrate this benefit. Our results show that EMVLight outperforms benchmark transportation engineering techniques as well as existing RL-based traffic signal control methods.

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Real-time deep reinforcement learning based vehicle navigation

Abstract: This is a repository copy of Real-time deep reinforcement learning based vehicle routing and navigation.

Cited by 78 publications

References 33 publications

Time-optimal and privacy preserving route planning for carpool policy

Time-optimal and privacy preserving route planning for carpool policy

Online No-regret Model-Based Meta RL for Personalized Navigation

A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles

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