Towards Safer Roads by Integration of Road Scene Monitoring and Vehicle Control

Fletcher, Luke; Zelinsky, Alexander; Barnes, Nick; Arnell, Fredrik

doi:10.1177/0278364906061156

“…3) Situation awareness enhancement: The CDI decisionmaking system combines a driver's gaze behavior and external driving scenario information to remind the driver of important information in driving scenario information and enhance the driver's SA. Fletcher et al [63] and Petersso et al [129] have proposed systems for alerting drivers about missed traffic signs. The system utilizes driver information, including head position and visual gaze direction, vehicle state information, including speed, acceleration, GPS, steering wheel angle, and driving scenario information to alert the driver.…”

Section: CDI Decision-making Systemmentioning

confidence: 99%

A Review of Cockpit-driving Integration for Human-centric Autonomous Driving

Zhu,

Zhao,

Wang

et al. 2023

Preprint

0

View full text Add to dashboard Cite

<p>This review focuses on the integration of intelligent driving and intelligent cockpit systems. With the advancement of intelligent levels, autonomous vehicles are capable of driving in complex driving context. However, the lack of systems that consider both driver and driving scenarios information reduces the success probability of decision-making of autonomous vehicles in driving context where driver and driving scenarios information are tightly coupled during the human-vehicle cooperation stage. To solve this problem, we present the Cockpit-Driving Integration system (CDI) and review perception and decision-making algorithms for CDI systems. Additionally, to achieve human-centric autonomous vehicles, we propose that the CDI system should consider the personalized characteristics of drivers. Finally, we present a framework for CDI systems </p>

show abstract

“…3) Situation awareness enhancement: The CDI decisionmaking system combines a driver's gaze behavior and external driving scenario information to remind the driver of important information in driving scenario information and enhance the driver's SA. Fletcher et al [63] and Petersso et al [129] have proposed systems for alerting drivers about missed traffic signs. The system utilizes driver information, including head position and visual gaze direction, vehicle state information, including speed, acceleration, GPS, steering wheel angle, and driving scenario information to alert the driver.…”

Section: CDI Decision-making Systemmentioning

confidence: 99%

A Review of Cockpit-driving Integration for Human-centric Autonomous Driving

Zhu,

Zhao,

Wang

et al. 2023

Preprint

0

View full text Add to dashboard Cite

<p>This review focuses on the integration of intelligent driving and intelligent cockpit systems. With the advancement of intelligent levels, autonomous vehicles are capable of driving in complex driving context. However, the lack of systems that consider both driver and driving scenarios information reduces the success probability of decision-making of autonomous vehicles in driving context where driver and driving scenarios information are tightly coupled during the human-vehicle cooperation stage. To solve this problem, we present the Cockpit-Driving Integration system (CDI) and review perception and decision-making algorithms for CDI systems. Additionally, to achieve human-centric autonomous vehicles, we propose that the CDI system should consider the personalized characteristics of drivers. Finally, we present a framework for CDI systems </p>

show abstract

“…Given a 3D world coordinate system, stereo analysis provides depth information about the traffic scene in form of a cloud of 3D points. After ground manifold estimation and possibly its removal [14,22], subsequent tasks are free-space estimation [2], obstacle detection using occupancy grids [16], or object segmentation by point clustering [17]. A tracking process involves motion analysis [4,15], e.g.…”

Section: Introductionmentioning

confidence: 99%

Mid-level Segmentation and Segment Tracking for Long-Range Stereo Analysis

Hermann

¹

,

Börner

²

,

Klette

³

2011

Advances in Image and Video Technology

4

0

View full text Add to dashboard Cite

This paper presents a novel way of combining dense stereo and motion analysis for the purpose of mid-level scene segmentation and object tracking. The input is video data that addresses long-range stereo analysis, as typical when recording traffic scenes from a mobile platform. The task is to identify shapes of traffic-relevant objects without aiming at object classification at the considered stage. We analyse disparity dynamics in recorded scenes for solving this task. Statistical shape models are generated over subsequent frames. Shape correspondences are established by using a similarity measure based on set theory. The motion of detected shapes (frame to frame) is compensated by using a dense motion field as produced by a real-time optical flow algorithm. Experimental results show the quality of the proposed method which is fairly simple to implement. The first author thanks the German Academic Exchange Service (DAAD) for financial support.

show abstract

Towards Safer Roads by Integration of Road Scene Monitoring and Vehicle Control

Cited by 13 publications

References 32 publications

A Review of Cockpit-driving Integration for Human-centric Autonomous Driving

A Review of Cockpit-driving Integration for Human-centric Autonomous Driving

A Review of Cockpit-driving Integration for Human-centric Autonomous Driving

Mid-level Segmentation and Segment Tracking for Long-Range Stereo Analysis

Contact Info

Product

Resources

About