| 1. |
- Erlandsson, Tina, et al.
(author)
-
Automatic evaluation of air mission routes with respect to combat survival
- 2014
-
In: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 20, s. 88-98
-
Journal article (peer-reviewed)abstract
- Aircraft flying in hostile environments are exposed to ground-based air defense systems. It is not always possible to both accomplish the mission and fly outside the range of the enemy’s weapon systems, especially if the positions of the enemy’s systems are not perfectly known. Automatic evaluation of mission routes from a combat survival perspective could therefore aid the pilots to plan their missions. When updated information regarding the positions and capabilities of the enemy’s systems is received during flight, the route could be re-evaluated and the mission could be re-planed or aborted if it is assessed to be too dangerous.The survivability model presented here describes the relation between the aircraft and the enemy’s defense systems. It calculates the probabilities that the aircraft is in certain modes along the route, e.g., undetected, tracked or hit. Contrary to previous work, the model is able to capture that the enemy’s systems can communicate and that the enemy must track the aircraft before firing a weapon. The survivability model is used to calculate an expected cost for the mission route. The expected cost has the attractive properties of summarizing the route into a single value and is able to take the pilot’s risk attitude for the mission into account. The evaluation of the route is influenced by uncertainty regarding the locations of the enemy’s sensors and weapons. Monte Carlo simulations are used to capture this uncertainty by calculating the mean and standard deviation for the expected cost. These two parameters give the pilots an assessment of the danger associated with the route as well as the reliability of this assessment. The paper concludes that evaluating routes with the survivability model and the expected cost could aid the pilots to plan and execute their missions.
|
|
| 2. |
- Lundquist, Christian, 1978-, et al.
(author)
-
Joint Ego-Motion and Road Geometry Estimation
- 2011
-
In: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 12:4, s. 253-263
-
Journal article (peer-reviewed)abstract
- We provide a sensor fusion framework for solving the problem of joint egomotion and road geometry estimation. More specifically we employ a sensor fusion framework to make systematic use of the measurements from a forward looking radar and camera, steering wheel angle sensor, wheel speed sensors and inertial sensors to compute good estimates of the road geometry and the motion of the ego vehicle on this road. In order to solve this problem we derive dynamical models for the ego vehicle, the road and the leading vehicles. The main difference to existing approaches is that we make use of a new dynamic model for the road. An extended Kalman filter is used to fuse data and to filter measurements from the camera in order to improve the road geometry estimate. The proposed solution has been tested and compared to existing algorithms for this problem, using measurements from authentic traffic environments on public roads in Sweden. The results clearly indicate that the proposed method provides better estimates.
|
|
| 3. |
- Nilsson, Maria, et al.
(author)
-
Information fusion in practice : A distributed cognition perspective on the active role of users
- 2012
-
In: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 13:1, s. 60-78
-
Journal article (peer-reviewed)abstract
- Traditionally, the focus of most information fusion research has been on computational aspects, as illustrated by, for example, different versions of the JDL data fusion model. Consequently, the human user has mainly been conceived as a relatively passive recipient of fused information. However, the importance of understanding the active role of human information processing in information fusion is gaining increasing recognition, as also reflected in discussions of a "level 5" in the JDL model. This paper presents a case study of the interaction between human and machine information processing in a maritime surveillance control room. A detailed analysis of cognitive processes and information flows involved in identifying and tracking moving vessels illustrates how machines and human operators collaboratively perform fusion in a highly distributed fashion. The theoretical framework of distributed cognition provides an alternative or complementary way of analysing information fusion systems/processes that more clearly reveals the actual complexities of the interaction between human and machine information processing in practice. (C) 2011 Elsevier B.V. All rights reserved.
|
|
| 4. |
- Sathyanarayana, Amardeep, et al.
(author)
-
Information Fusion for Context and Driver Aware Active Vehicle Safety Systems
- 2011
-
In: Information Fusion. - : Elsevier BV. - 1566-2535. ; 12:4, s. 293-303
-
Journal article (peer-reviewed)abstract
- Although there is currently significant development in active vehicle safety (AVS) systems, the number of accidents, injury severity levels and fatalities has not reduced. In fact, human error, low performance, drowsiness and distraction may account for a majority in all the accident causation. Active safety systems are unaware of the context and driver status, so these systems cannot improve these figures. Therefore, this study proposes a ‘context and driver aware’ (CDA) AVS system structure as a first step in realizing robust, human-centric and intelligent active safety systems. This work develops, evaluates and combines three sub-modules all employing a Gaussian Mixture Model (GMM)/Universal Background Model (UBM) and likelihood maximization learning scheme: biometric driver identification, maneuver recognition, and distraction detection. The resultant combined system contributes in three areas: (1) robust identification: a speaker recognition system is developed in an audio modality to identify the driver in-vehicle conditions requiring robust operation; (2) narrow the available information space for fusion: maneuver recognition system uses estimated driver identification to prune the selection of models and further restrict search space in a novel distraction detection system; (3) response time and performance: the system quickly produces a prediction of driver’s distracted behaviour for possible use in accident prevention/avoidance. Overall system performance of the combined system is evaluated on the UTDrive Corpus, confirming the suitability of the proposed system for critical imminent accident cases with narrow time windows.
|
|
