\n
![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F636669%2Fb9fbbd70d96c795ec665d8eed4033c1c%2FScreenshot%20from%202020-09-11%2020-03-50.png?generation=1599840294925721&alt=media\")
Paper link : https://arxiv.org/pdf/1912.11676.pdf referred by @ffares . Thank you!
\nSummary
\nFor safe and efficient operation on roads, an Autonomous Vehicle (AV) should not only understand the current state of the nearby road-users, but also proactively anticipate their future behaviour.
\nvehicles’ behaviour is constrained by their higher inertia, driving rules, road geometry, interdependency among vehicles behaviour, the influence of traffic rules and driving environment.
“Survey on vehicle behaviour prediction and risk assessment in the context of autonomous vehicles” - Lefevre et al.” In this paper, the authors review various conventional approaches that applied physics-based models and/or traditional machine learning algorithms such as Hidden Markov Models, Support Vector Machines, and Dynamic Bayesian Networks.
\nBelieve that recent advances in machine learning techniques (EX: deep learning) have provided new and powerful tools for solving the problem of vehicle behaviour prediction. Since there is no systematic and comparative review of later deep learning-based approaches. Presented a review of such studies using a new classification method which is based on three criteria :
\nBefore review and classify deep-learning based models based on the above classes, research team mentioned challenges, basic terminology and generic problem formulation.
\nHere we go,
\nVehicle behaviour prediction is not a trivial task due to several challenges.
\nRequired terminology to understand further.
\nClassification of deep learning models under three classes as the research team said at initial.
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F636669%2F3c2db7aa2bc338dd336e30e222f7d738%2FScreenshot%20from%202020-09-11%2020-10-15.png?generation=1599840413789654&alt=media\")
\n Study based on input representation
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F636669%2F9e04006dd5b2fa2f40e924755277d8db%2FScreenshot%20from%202020-09-11%2020-19-46.png?generation=1599871960155127&alt=media\")
\nStudy based on output type
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F636669%2Fe835f16c11e85635640a10d14a3ac6bb%2FScreenshot%20from%202020-09-11%2020-20-03.png?generation=1599872073124213&alt=media\")
\nStudy based on prediction method
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F636669%2F8266474d58ad8c00498721741ff71423%2FScreenshot%20from%202020-09-11%2020-20-38.png?generation=1599872189402101&alt=media\")
Paper link : https://arxiv.org/pdf/1912.11676.pdf referred by @ffares . Thank you!
\nSummary
\nFor safe and efficient operation on roads, an Autonomous Vehicle (AV) should not only understand the current state of the nearby road-users, but also proactively anticipate their future behaviour.
\nvehicles’ behaviour is constrained by their higher inertia, driving rules, road geometry, interdependency among vehicles behaviour, the influence of traffic rules and driving environment.
“Survey on vehicle behaviour prediction and risk assessment in the context of autonomous vehicles” - Lefevre et al.” In this paper, the authors review various conventional approaches that applied physics-based models and/or traditional machine learning algorithms such as Hidden Markov Models, Support Vector Machines, and Dynamic Bayesian Networks.
\nBelieve that recent advances in machine learning techniques (EX: deep learning) have provided new and powerful tools for solving the problem of vehicle behaviour prediction. Since there is no systematic and comparative review of later deep learning-based approaches. Presented a review of such studies using a new classification method which is based on three criteria :
\nBefore review and classify deep-learning based models based on the above classes, research team mentioned challenges, basic terminology and generic problem formulation.
\nHere we go,
\nVehicle behaviour prediction is not a trivial task due to several challenges.
\nRequired terminology to understand further.
\nClassification of deep learning models under three classes as the research team said at initial.
\n
\n Study based on input representation
\n
\nStudy based on output type
\n
\nStudy based on prediction method
\n
Thank you for breaking this down!! Bookmarked :-))
", "rawMarkdown": "Thank you for breaking this down!! Bookmarked :-))", "votes": null } ], "comments": [ { "id": 1029113, "author_name": "fireheart7", "author_url": "", "post_date": "09/27/2020 13:22:36", "content": "Thank you for breaking this down!! Bookmarked :-))
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1006904": "Paper link : https://arxiv.org/pdf/1912.11676.pdf referred by @ffares . Thank you!\n\nSummary\n\nFor safe and efficient operation on roads, an Autonomous Vehicle (AV) should not only understand the current state of the nearby road-users, but also proactively anticipate their future behaviour. \nvehicles’ behaviour is constrained by their higher inertia, driving rules, road geometry, interdependency among vehicles behaviour, the influence of traffic rules and driving environment.\n\n“**Survey on vehicle behaviour prediction and risk assessment in the context of autonomous vehicles” - Lefevre et al.**” In this paper, the authors review various conventional approaches that applied physics-based models and/or traditional machine learning algorithms such as Hidden Markov Models, Support Vector Machines, and Dynamic Bayesian Networks.\n\nBelieve that recent advances in machine learning techniques (EX: deep learning) have provided new and powerful tools for solving the problem of vehicle behaviour prediction. Since there is no systematic and comparative review of later deep learning-based approaches. Presented a review of such studies using a new classification method which is based on three criteria :\n- Input representation,\n- Output type, and \n- Prediction model\n\nBefore review and classify deep-learning based models based on the above classes, research team mentioned challenges, basic terminology and generic problem formulation. \n\nHere we go,\n\nVehicle behaviour prediction is not a trivial task due to several challenges. \n1. There is an interdependency among vehicles behaviour where the behaviour of a vehicle affects the behaviour of other vehicles and vice versa. Therefore, predicting the behaviour of a vehicle requires observing the behaviour of surrounding vehicles. \n2. Road geometry and traffic rules can reshape the behaviour of vehicles. For example, placing a give-way sign in an intersection can completely change the behaviour of vehicles approaching it. Therefore, without considering traffic rules and road geometry, a model trained in a specific driving environment would have limited performance in other driving environments. \n3. The future behaviour of vehicles is multimodal, meaning that given the history of motion of a vehicle, there may exist more than one possible future behaviour for it. For example, when a vehicle is slowing down at an intersection without changing its heading direction, both turning right and turning left motions could be expected. A comprehensive behaviour prediction module in an autonomous vehicle should identify all possible future motions to allow the vehicle to act reliably.\n\nRequired terminology to understand further.\n\n- **Target Vehicles** (TVs) are the vehicles whose behaviour we are interested in predicting.\n- **Ego Vehicle** (EV) is the autonomous vehicle which observes the surrounding environment to predict the behaviour of TVs.\n- **Surrounding Vehicles** (SVs) are the vehicles whose behaviour is explored by the prediction model as it can potentially impact TV’s future behaviour. Different studies may adopt different criteria for selecting SVs based on their modelling assumptions.\n- **Non-Effective Vehicles** (NVs) are the remaining vehicles in the driving environment that are assumed to have no impact on the TV’s behaviour.\n\n\nClassification of deep learning models under three classes as the research team said at initial.\n\n\n Study based on input representation\n\nStudy based on output type \n\nStudy based on prediction method\n", "1029113": "Thank you for breaking this down!! Bookmarked :-))" }, "source": "meta" }