ROAD++: The Third Workshop & Challenge: Event Detection for Situation Awareness in Autonomous Driving
Co-hosted by ECCV 2024
Sep 29 , MiCo Milano
Workshop Agenda
Half day workshop at afternoon 29th of September 2024
Overview
This includes the following tasks:
• Detecting and modeling ‘atomic’ events, intended as simple actions performed by a single agent. • Detecting and modeling complex activities, contributed to by several agents over an extended period of time. • Predicting agent intentions. • Predicting the trajectory of pedestrians, vehicles, and other road users. • Forecasting future road events (both atomic and complex). • Based on all these elements, deciding what action the autonomous vehicle should perform next. • Modelling the reasoning processes of road agents in terms of goals or mental states. • Realistic simulation to generate training data for road event action detection.
We invite both paper contributions on these topics, as well as submissions of entries to a challenge specifically designed to test situation awareness capabilities in autonomous vehicles.
Submitted papers will follow the standard ECCV 2024 template (here). Authors are welcome to submit a supplementary material document with details on their implementation; however, reviewers are not required to consult this additional material when assessing the submission. The Workshop will allow for the submission of papers concurrently submitted elsewhere, with the aim of aggregating all relevant efforts in this area.
Accepted papers will get a poster at the main conference, and the very best ones also an oral.
The accurate detection and anticipation of actions performed by multiple road agents (pedestrians, vehicles, cyclists and so on) is a crucial task to address for enabling autonomous vehicles to make autonomous decisions in a safe, reliable way. While the task of teaching an autonomous vehicle how to drive can be tackled in a brute-force fashion through direct reinforcement learning, a sensible and attractive alternative is to first provide the vehicle with situation awareness capabilities, to then feed the resulting semantically meaningful representations of road scenarios (in terms of agents, events and scene configuration) to a suitable decision-making strategy. In perspective, this has also the advantage of allowing the modeling of the reasoning process of road agents in a theory-of-mind approach, inspired by the behaviour of the human mind in similar contexts.
We also introduce atomic activity recognition, from action recognition and video understanding perspectives. The atomic activities are road-topology-grounded actions of the agents. This enables a more expressive and efficient traffic scenario retrieval, which can facilitate other applications, such as safety-critical scenario generation.
Accordingly, the goal of this Challenge is to put to the forefront of the research in autonomous driving the topic of situation awareness, intended as the ability to create semantically useful representations of dynamic road scenes, in terms of the notion of a road event.
To allow the research community to thoroughly investigate situation awareness for autonomous driving, this workshop introduces ROAD++, an extension of the first ROad event Awareness in Autonomous Driving Dataset. It is the result of annotating: a combination of four datasets from different domains including ROAD (UK), ROAD-Waymo (USA), ROAD-UAE (UAE), and TACO (CARLA simulation) dataset in terms of what we call road events (REs), as seen from the point of view of the autonomous vehicle capturing the video. REs are defined as triplets E = (Ag;Ac; Loc) composed by a moving agent Ag, the action Ac it performs, and the location Loc in which this takes place. Agent, action and location are all classes in a finite list compiled by surveying the content of the videos.
More information about the dataset can be found the the dataset tab or directly from here (TBA)
Invited Speakers
Prof. Holger Caesar
TU Delft, Netherlands
Prof. Manmohan Chandraker
UCSD, USA
Organization Committee
Prof. Fabio Cuzzolin
Oxford Brookes University
Dr. Salman Khan
Oxford Brookes University
Dr. Reza Javanmard Alitappeh
University of Science and Technology of Mazandaran
Dr. Eleonora Giunchiglia
TU Wien
Oxford Brookes University
Mihaela Catalina Stoian
University of Oxford
Dr. Andrew Bradley
Oxford Brookes University
Dr. Gurkirt Singh
Swiss Federal Institute of Technology in Zurich
Prof. Jorge Dias
Khalifa University
Nadya Abdel Madjid
Khalifa University
Dr. Majid Khonji
Khalifa University
Dr. Bilal Hassan
Khalifa University
National Yang Ming Chiao Tung University
National Yang Ming Chiao Tung University