Multi-Sensor Fusion in Automated Driving: A Survey

Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

Section: Perspective On Sensor Fusionmentioning

confidence: 99%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

“…However, in order to get a good estimate of the posterior it is required that detections were present in the time instances leading up to the missing detection. Since we cannot sample directly from the updated posterior p X,C,A|Z (x t , c t , a t |z t ) (due to missing observation: z t = ) we compute use an approximation by estimating posterior with no regard for missing data using Equation (28). This means that the particles x t−1 .…”

Section: Handling Missing Detectionsmentioning

confidence: 99%

Cooperative Multi-Sensor Tracking of Vulnerable Road Users in the Presence of Missing Detections

Dimitrievski

Hamme

Veelaert

et al. 2020

This paper presents a vulnerable road user (VRU) tracking algorithm capable of handling noisy and missing detections from heterogeneous sensors. We propose a cooperative fusion algorithm for matching and reinforcing of radar and camera detections using their proximity and positional uncertainty. The belief in the existence and position of objects is then maximized by temporal integration of fused detections by a multi-object tracker. By switching between observation models, the tracker adapts to the detection noise characteristics making it robust to individual sensor failures. The main novelty of this paper is an improved imputation sampling function for updating the state when detections are missing. The proposed function uses a likelihood without association that is conditioned on the sensor information instead of the sensor model. The benefits of the proposed solution are two-fold: firstly, particle updates become computationally tractable and secondly, the problem of imputing samples from a state which is predicted without an associated detection is bypassed. Experimental evaluation shows a significant improvement in both detection and tracking performance over multiple control algorithms. In low light situations, the cooperative fusion outperforms intermediate fusion by as much as 30%, while increases in tracking performance are most significant in complex traffic scenes.

“…We think that the automated driving sector represents a highly significant investigation domain given the huge amount of research that is being carried out in the field (e.g., [4][5][6]). As an example use case, we thus discuss our experience in a 34-partner EU-funded project, L3Pilot, which is assessing the impact of Society of Automotive Engineers (SAE) Level 3 (L3) and Level 4 (L4) automated driving functions (ADFs).…”

Section: Introductionmentioning

confidence: 99%

Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment

Bellotti

Osman

Arnold

et al. 2020

While extracting meaningful information from big data is getting relevance, literature lacks information on how to handle sensitive data by different project partners in order to collectively answer research questions (RQs), especially on impact assessment of new automated driving technologies. This paper presents the application of an established reference piloting methodology and the consequent development of a coherent, robust workflow. Key challenges include ensuring methodological soundness and data validity while protecting partners’ intellectual property. The authors draw on their experiences in a 34-partner project aimed at assessing the impact of advanced automated driving functions, across 10 European countries. In the first step of the workflow, we captured the quantitative requirements of each RQ in terms of the relevant data needed from the tests. Most of the data come from vehicular sensors, but subjective data from questionnaires are processed as well. Next, we set up a data management process involving several partners (vehicle manufacturers, research institutions, suppliers and developers), with different perspectives and requirements. Finally, we deployed the system so that it is fully integrated within the project big data toolchain and usable by all the partners. Based on our experience, we highlight the importance of the reference methodology to theoretically inform and coherently manage all the steps of the project and the need for effective and efficient tools, in order to support the everyday work of all the involved research teams, from vehicle manufacturers to data analysts.