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Scenario Analysis & Data Fusion Specification Panagiotis Lytrivis ICCS

Scenario Analysis & Data Fusion Specification Panagiotis Lytrivis ICCS panagiotis.lytrivis@iccs.gr. SAFEPROBE. Scenario Analysis. Scenario 13 “Ego-vehicle starts braking very hard while other vehicle is following”.

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Scenario Analysis & Data Fusion Specification Panagiotis Lytrivis ICCS

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  1. Scenario Analysis & Data Fusion Specification Panagiotis Lytrivis ICCS panagiotis.lytrivis@iccs.gr SAFEPROBE

  2. Scenario Analysis • Scenario 13 • “Ego-vehicle starts braking very hard while other vehicle is following” • Simulations based on a backward looking mounting sensor (LRR, Laserscanner, vision)

  3. Scenario Analysis (2) • Scenario 14 • “Ego-vehicle overtakes a vehicle changing lane and entering into a collision path with another oncoming vehicle” • We used 2 LRRs for forward sensing • FOV 40o • Range 60m

  4. Task 1.3.2 - Environment Data Acquisition Specification • Interfaces and protocols are defined in SP3-SINTECH • In this task we analyze the “data packet” specs • Important issues: • Data flow between V2V or V2I • Time management & time-stamping • Data storage management • Output: A complete protocol for data acquisition and storage, • before processing and data fusion

  5. Task 1.3.3 - Cooperative Data Fusion Specification • We have changed this task and “Task 1.3.1 – Internal Data Fusion Specification” (according to JDL model) into • Object refinement and • Situation refinement • The same idea is followed in ProFusion • Change the Technical Annex? • Yes→ Do it the right way and not just replace the task titles • No→ The distinction between the 2 tasks will be vague, but the necessary will definitely get done • One proposal: Change it together with some other changes that had already been discussed in the Core Group

  6. Object refinement • Key functions: • Feature extraction (Segmentation, contour extraction etc.) • Object tracking • Observation-to-track-association • Track maintenance • Object classification •  The output of the object refinement consists of a list of objects with specific parameters or attributes (i.e. position, velocity)  Object refinement aims at the representation of the environment through the LDM (vehicles, pedestrians, road borders etc.) Object tracking & data fusion from distributed sensor nodes (cooperative fusion) for a common state estimation of the objects Data association problem

  7. Situation refinement • It is more a relational matter between the detected objects • how they interact, • what scenario they represent etc. •  The most common problem is the path or trajectory prediction: • We take the history of an object’s state (its position and respective speed in the past) and we try to predict the object’s state in the near term future •  Outputs of all distributed sensors are fused to create an overall situational understanding (environmental model), which is then available to any applications running on the vehicle

  8. Situation refinement (2) • We have experience in situation refinement, especially in maneuver detection (detection of the intention of the driver and lane assignment of the detected object) • We are involved in ProFusion (PReVENT SP) •  Hard task because it is a relational matter between objects and we need advanced machine intelligence • Example: “Object vehicle in left lane is in the process of overtaking and passing a preceding vehicle” • For the situation refinement: • What scenarios should the system be aware of? • What’s the level of difficulty of the implementation? • To do that we need input from scenario analysis and from applications • Also from side of architecture which queries should we answer?

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