| 1. |
- Aramrattana, Maytheewat, et al.
(författare)
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Dimensions of cooperative driving, ITS and automation
- 2015
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Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. - Piscataway, NJ : IEEE Press. - 9781467372664 ; , s. 144-149
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Konferensbidrag (refereegranskat)abstract
- Wireless technology supporting vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I) communication, allow vehicles and infrastructures to exchange information, and cooperate. Cooperation among the actors in an intelligent transport system (ITS) can introduce several benefits, for instance, increase safety, comfort, efficiency.Automation has also evolved in vehicle control and active safety functions. Combining cooperation and automation would enable more advanced functions such as automated highway merge and negotiating right-of-way in a cooperative intersection. However, the combination have influences on the structure of the overall transport systems as well as on its behaviour. In order to provide a common understanding of such systems, this paper presents an analysis of cooperative ITS (C-ITS) with regard to dimensions of cooperation. It also presents possible influence on driving behaviour and challenges in deployment and automation of C-ITS.
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| 2. |
- Barnada, Marc, et al.
(författare)
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Estimation of Automotive Pitch, Yaw, and Roll using Enhanced Phase Correlation on Multiple Far-field Windows
- 2015
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Ingår i: 2015 IEEE Intelligent Vehicles Symposium (IV). - : IEEE. - 9781467372664 ; , s. 481-486
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Konferensbidrag (refereegranskat)abstract
- The online-estimation of yaw, pitch, and roll of a moving vehicle is an important ingredient for systems which estimate egomotion, and 3D structure of the environment in a moving vehicle from video information. We present an approach to estimate these angular changes from monocular visual data, based on the fact that the motion of far distant points is not dependent on translation, but only on the current rotation of the camera. The presented approach does not require features (corners, edges,...) to be extracted. It allows to estimate in parallel also the illumination changes from frame to frame, and thus allows to largely stabilize the estimation of image correspondences and motion vectors, which are most often central entities needed for computating scene structure, distances, etc. The method is significantly less complex and much faster than a full egomotion computation from features, such as PTAM [6], but it can be used for providing motion priors and reduce search spaces for more complex methods which perform a complete analysis of egomotion and dynamic 3D structure of the scene in which a vehicle moves.
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| 3. |
- Bergquist, Stefan, 1982, et al.
(författare)
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On threat assessment and collision avoidance for articulated machinery in low-speed scenarios
- 2015
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Ingår i: Proceedings of IEEE Intelligent Vehicles Symposium, IV 2015. - 9781467372664 ; 2015 -August, s. 1213-1219
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Konferensbidrag (refereegranskat)abstract
- - This paper investigates the hypothesis that threat assessment algorithms, developed for passenger cars and modified for articulated vehicles in low-speed scenarios, can be used to avoid a majority of the collision accidents in construction environments. The effect of using a collision avoidance system on a Volvo A25 hauler is investigated in several simulated scenarios with stationary obstacles. It is concluded that the safety benefit is severely limited by the relatively low braking capacity of the machine. Due to the limited capacity, braking manoeuvres must be initiated rather early, therefore steering is more efficient in many situations. Moreover, collisions are difficult to avoid when there is an offset between the machine and obstacle which is likely to occur in unstructured environments. The effect of different driving styles is also analysed and adapting the collision avoidance function to the individual driver can increase the number of collisions prevented. Another possible way to increase the safety benefit is to introduce a safety margin around obstacles. Both these options, however, increase the risk for generating false alarms. © 2015 IEEE.
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| 4. |
- Chen, Lei, et al.
(författare)
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Coordinating dangerous goods vehicles : C-ITS applications for safe road tunnels
- 2015
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Ingår i: 2015 IEEE Intelligent Vehicles Symposium (IV). - Piscataway, NJ : IEEE. - 9781467372664 - 9781467372657 ; , s. 156-161
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Konferensbidrag (refereegranskat)abstract
- Despite the existing regulation efforts and measures, vehicles with dangerous goods still pose significant risks on public safety, especially in road tunnels. Solutions based on cooperative intelligent transportation system (C-ITS) are promising measures, however, they have received limited attention. We propose C-ITS applications that coordinate dangerous goods vehicles to minimize the risk by maintaining safe distances between them in road tunnels. Different mechanisms, including global centralized coordination, global distributed coordination, and local coordination, are proposed and investigated. A preliminary simulation is performed and demonstrates their effectiveness. © 2015 IEEE.
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| 5. |
- Evestedt, Niclas, 1985-, et al.
(författare)
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Sampling Recovery for Closed Loop Rapidly Expanding Random Tree using Brake Profile Regeneration
- 2015
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Ingår i: Intelligent Vehicles Symposium (IV), 2015 IEEE. - : IEEE. - 9781467372664 ; , s. 101-106
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Konferensbidrag (refereegranskat)abstract
- In this paper an extension to the sampling based motion planning framework CL-RRT is presented. The framework uses a system model and a stabilizing controller to sample the perceived environment and build a tree of possible trajectories that are evaluated for execution. Complex system models and constraints are easily handled by a forward simulation making the framework widely applicable. To increase operational safety we propose a sampling recovery scheme that performs a deterministic brake profile regeneration using collision information from the forward simulation. This greatly increases the number of safe trajectories and also reduces the number of samples that produce infeasible results. We apply the framework to a Scania G480 mining truck and evaluate the algorithm in a simple yet challenging obstacle course and show that our approach greatly increases the number of feasible paths available for execution.
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| 6. |
- Fu, Keren, 1988, et al.
(författare)
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Traffic Sign Recognition using Salient Region Features: A Novel Learning-based Coarse-to-Fine Scheme
- 2015
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Ingår i: IEEE Intelligent Vehicles Symposium, June 28-July 1, 2015, Seoul, Korea. - 9781467372664 ; , s. 443-448
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Konferensbidrag (refereegranskat)abstract
- Traffic sign recognition, including sign detection and classification, is essential for advanced driver assistancesystems and autonomous vehicles. This paper introduces a novel machine learning-based sign recognition scheme. In the proposed scheme, detection and classification are realized through learning in a coarse-to-fine manner. Based on the observation that signs in the same category share some common attributes in appearance, the proposed scheme first distinguishes each individual sign category from the background in the coarse learning stage (i.e. sign detection) followed by distinguishing different sign classes within each category in the fine learning stage (i.e. sign classification). Both stages are realized throughmachine learning techniques. A complete recognition scheme is developed that is effective for simultaneously recognizing multiple categories of traffic signs. In addition, a novel saliency-based feature extraction method is proposed for sign classification. The method segments salient sign regions by leveraging the geodesic energy propagation. Compared with the conventional feature extraction, our method provides more reliable feature extraction from salient sign regions. The proposed scheme istested and validated on two categories of Chinese traffic signs from Tencent street view. Evaluations on the test dataset show reasonably good performance, with an average of 97.5% true positive and 0.3% false positive on two categories of traffic signs.
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| 7. |
- Islam, Manjurul, 1984, et al.
(författare)
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Inverse Model Control Including Actuator Dynamics for Active Dolly Steering in High Capacity Transport Vehicle
- 2015
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Ingår i: Intelligent Vehicles Symposium (IV), 2015 IEEE. - 9781467372664 ; , s. 1024 - 1031
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Konferensbidrag (refereegranskat)abstract
- This paper describes an advance controller designed using the nonlinear inversion technique of a Modelica based simulation tool, such as Dymola, for active dolly steering of a high capacity transport vehicle. Actuator dynamics is included in the inverse model controller. Therefore, it can automatically generate required steering angle request for the dolly axles of the vehicle combination. The resultant controller is transfered as a functional mock-up unit (FMU) to Simulink environment where the actual simulations are conducted. The controller is simulated against a high-fidelity vehicle model of an A-double combination from Virtual Truck Models (VTM) library -- developed by Volvo Group Trucks Technology. Effects of variations of the actual actuator dynamics, with respect to the modeled dynamics in the inverse model controller, on overall vehicle performance are investigated.
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| 8. |
- Li, G., et al.
(författare)
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Lane change maneuver recognition via vehicle state and driver operation signals - Results from naturalistic driving data
- 2015
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Ingår i: Proceedings IEEE Intelligent Vehicles Symposium, IV 2015. - 9781467372664 ; 2015-August, s. 865-870
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Konferensbidrag (refereegranskat)abstract
- - Lane change maneuver recognition is critical in driver characteristics analysis and driver behavior modeling for active safety systems. This paper presents an enhanced classification method to recognize lane change maneuver by using optimized features exclusively extracted from vehicle state and driver operation signals. The sequential forward floating selection (SFFS) algorithm was adopted to select the optimized feature set to maximize the k-nearest-neighbor classifier performance. The hidden Markov models (HMMs), based on the optimized feature set, were developed to classify driver lane change and lane keeping maneuvers. Fifteen drivers participated in the road test for validation with an accumulation of 2,200 km naturalistic driving data, from which 372 lane changes were extracted. Results show that the recognition rate of lane change maneuver achieves 88.2%. The numbers are 87.6% and 88.8% for left and right lane change maneuvers, respectively, superior to the results from conventional classifiers. © 2015 IEEE.
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| 9. |
- Persson, Mikael, 1985-, et al.
(författare)
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Robust Stereo Visual Odometry from Monocular Techniques
- 2015
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Ingår i: 2015 IEEE Intelligent Vehicles Symposium (IV). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467372664 ; , s. 686-691
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Konferensbidrag (refereegranskat)abstract
- Visual odometry is one of the most active topics in computer vision. The automotive industry is particularly interested in this field due to the appeal of achieving a high degree of accuracy with inexpensive sensors such as cameras. The best results on this task are currently achieved by systems based on a calibrated stereo camera rig, whereas monocular systems are generally lagging behind in terms of performance. We hypothesise that this is due to stereo visual odometry being an inherently easier problem, rather than than due to higher quality of the state of the art stereo based algorithms. Under this hypothesis, techniques developed for monocular visual odometry systems would be, in general, more refined and robust since they have to deal with an intrinsically more difficult problem. In this work we present a novel stereo visual odometry system for automotive applications based on advanced monocular techniques. We show that the generalization of these techniques to the stereo case result in a significant improvement of the robustness and accuracy of stereo based visual odometry. We support our claims by the system results on the well known KITTI benchmark, achieving the top rank for visual only systems∗ .
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| 10. |
- Savino, G., et al.
(författare)
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Triggering Algorithm based on Inevitable Collision States for Autonomous Emergency Braking (AEB) in Motorcycle-to-Car Crashes
- 2015
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Ingår i: 2015 IEEE Intelligent Vehicles Symposium (IV). - 9781467372664 ; , s. 1195-1200
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Konferensbidrag (refereegranskat)abstract
- This study presents a triggering algorithm for a collaborative, motorcycle-to-car collision avoidance system that slows down the car without input of the driver when the collision becomes imminent. The algorithm is based on the concept of inevitable state collisions. Example applications of the proposed algorithm were obtained via 2D computer simulations representing a data set of real crashes occurred in Italy, Sweden and Australia. Results indicated that the proposed method can apply to typical crash scenarios
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