Parallelized and randomized adversarial imitation learning for safety-critical self-driving vehicles

Yun, Won Joon; Shin, MyungJae; Jung, Soyi; Kwon, Sean Seok-Chul; Kim, Joongheon

doi:10.23919/jcn.2022.000012

Cited by 4 publications

(2 citation statements)

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“…Moreover, they modified the enhanced AIRL to a more demanding decision-making task in an environment that is highly interactive for autonomous driving. In another study Yun et al [ 97 ] presented a randomized adversarial imitation learning (RAIL) algorithm. The RAIL is a new imitation learning technique that does not use any derivatives and is designed to mimic the coordination of several advanced driver assistance systems (ADAS) functions while driving autonomously.…”

Section: The Analyses Of Decision-making Relevant Solutions For Auton...mentioning

confidence: 99%

How Do Autonomous Vehicles Decide?

Malik

Khan

El-Sayed

et al. 2022

Sensors

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The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in traffic accidents yearly, where the vast majority of the accidents are caused by human negligence. Higher-level autonomous driving has great potential to enhance road safety and traffic efficiency. One of the most crucial links to building an autonomous system is the task of decision-making. The ability of a vehicle to make robust decisions on its own by anticipating and evaluating future outcomes is what makes it intelligent. Planning and decision-making technology in autonomous driving becomes even more challenging, due to the diversity of the dynamic environments the vehicle operates in, the uncertainty in the sensor information, and the complex interaction with other road participants. A significant amount of research has been carried out toward deploying autonomous vehicles to solve plenty of issues, however, how to deal with the high-level decision-making in a complex, uncertain, and urban environment is a comparatively less explored area. This paper provides an analysis of decision-making solutions approaches for autonomous driving. Various categories of approaches are analyzed with a comparison to classical decision-making approaches. Following, a crucial range of research gaps and open challenges have been highlighted that need to be addressed before higher-level autonomous vehicles hit the roads. We believe this survey will contribute to the research of decision-making methods for autonomous vehicles in the future by equipping the researchers with an overview of decision-making technology, its potential solution approaches, and challenges.

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Section: The Analyses Of Decision-making Relevant Solutions For Auton...mentioning

confidence: 99%

How Do Autonomous Vehicles Decide?

Malik

Khan

El-Sayed

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…As shown in Fig. 2, based on the time n that the vehicle requires a resource allocation, the time domain is divided into the sensing window of [n − 1000 ms, n] and the selection window of [n + T 1, n + T 2], where T 1 is in [1,4] ms, and T 2 is in [20,100] ms. Each vehicle performs four steps for occupying the BR and transmitting the BSM beacon as follows:…”

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confidence: 99%