Steering and Evasion Assist

Dang, Thao; Desens, J.; Franke, Uwe; Gavrila, Dariu M.; Schäfers, Lorenz; Ziegler, Walter

doi:10.1007/978-0-85729-085-4_29

Cited by 26 publications

(16 citation statements)

References 19 publications

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“…Examples include an assistant for collision avoidance by evasive steering (Dang et al 2012 This approach promises an extension of a system's boundaries with respect to the availability of information and the expansion of its function to an entire collective of road users allowing for assisted or automated cooperative maneuvering , Shladover 2009). …”

Section: Vehicle Dynamics Stabilizationmentioning

confidence: 99%

Three Decades of Driver Assistance Systems: Review and Future Perspectives

Bengler

Dietmayer

Färber

et al. 2014

IEEE Intell. Transport. Syst. Mag.

740

325

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This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.

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Section: Vehicle Dynamics Stabilizationmentioning

confidence: 99%

Three Decades of Driver Assistance Systems: Review and Future Perspectives

Bengler

Dietmayer

Färber

et al. 2014

IEEE Intell. Transport. Syst. Mag.

740

325

View full text Add to dashboard Cite

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“…Recently, increasing attention has been dedicated to VRUs safety (e.g., [3], [9]- [11], [14], [15]). In [9], a joint team from Daimler and Karlsruhe Institute of Technology drove an autonomous car on the Bertha Benz Memorial Route, where they had to deal with VRUs.…”

Section: Related Workmentioning

confidence: 99%

“…Then, a decision module decides whether to warn the driver or to trigger the appropriate maneuvers for collision avoidance and mitigation using dedicated controllers. In [11], the authors provide an overview of evasive steering techniques discussing the potential of evasive steering vs. braking. In addition, they also detail the design of the Daimler automatic evasion driver-assistance system for pedestrian protection.…”

Section: Related Workmentioning

confidence: 99%

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SafeVRU: A Research Platform for the Interaction of Self-Driving Vehicles with Vulnerable Road Users

Ferranti

Brito

Pool

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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This paper presents our research platform SafeVRU for the interaction of self-driving vehicles with Vulnerable Road Users (VRUs, i.e., pedestrians and cyclists). The paper details the design (implemented with a modular structure within ROS) of the full stack of vehicle localization, environment perception, motion planning, and control, with emphasis on the environment perception and planning modules. The environment perception detects the VRUs using a stereo camera and predicts their paths with Dynamic Bayesian Networks (DBNs), which can account for switching dynamics. The motion planner is based on model predictive contouring control (MPCC) and takes into account vehicle dynamics, control objectives (e.g., desired speed), and perceived environment (i.e., the predicted VRU paths with behavioral uncertainties) over a certain time horizon. We present simulation and real-world results to illustrate the ability of our vehicle to plan and execute collision-free trajectories in the presence of VRUs. I. INTRODUCTION Every year between 20 and 50 million people are involved in road accidents, mostly caused by human errors [1]. According to [1], approximately 1.3 million people lost their life in these accidents. Half of the victims are vulnerable road users (VRUs), such as pedestrians and cyclists. Self-driving vehicles can help reduce these fatalities [2]. Active safety features, such as autonomous emergency braking (AEB), are increasingly found on-board vehicles on the market to improve VRUs' safety (see [3] for a recent overview). In addition, some vehicles already automate steering functionality (e.g., [4], [5]), but still require the driver to initiate the maneuver. Major challenges must be addressed to ensure safety and performance while driving in complex urban environments [6], where VRUs are present. The self-driving vehicle should be aware of the presence of the VRUs and be able to infer their intentions to plan its path accordingly to avoid collisions. In this respect, motion planning methods are required to provide safe (collision-free) and systemcompliant performance in complex environments with static and moving obstacles (refer to [7], [8] for an overview). In real-world applications, the information on the pose (i.e., position and orientation) of other traffic participants comes from a perception module. The perception module should provide to the planner information not only concerning the current position of the other road users, but also † The authors equally contributed to the paper.

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“…O VER the past decade, the essential role of machine vision modules to realize active safety systems for accident prevention is clearly established in academic research [1], [2] and is also reflected in innovative systems introduced by industry [3], [4]. Effective vision systems need to accurately assess situational criticalities from the panoramic surround of a vehicle [5] and simultaneously assess awareness of these criticalities by the driver [6].…”

Section: Introductionmentioning

confidence: 99%

Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation

Prioletti

Møgelmose

Grisleri

et al. 2013

IEEE Trans. Intell. Transport. Syst.

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Abstract-Detecting pedestrians is still a challenging task for automotive vision systems due to the extreme variability of targets, lighting conditions, occlusion, and high-speed vehicle motion. Much research has been focused on this problem in the last ten years and detectors based on classifiers have gained a special place among the different approaches presented. This paper presents a state-of-the-art pedestrian detection system based on a two-stage classifier. Candidates are extracted with a Haar cascade classifier trained with the Daimler Detection Benchmark data set and then validated through a part-based histogram-of-orientedgradient (HOG) classifier with the aim of lowering the number of false positives. The surviving candidates are then filtered with feature-based tracking to enhance the recognition robustness and improve the results' stability. The system has been implemented on a prototype vehicle and offers high performance in terms of several metrics, such as detection rate, false positives per hour, and frame rate. The novelty of this system relies on the combination of a HOG part-based approach, tracking based on a specific optimized feature, and porting on a real prototype.

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Steering and Evasion Assist

Cited by 26 publications

References 19 publications

Three Decades of Driver Assistance Systems: Review and Future Perspectives

Three Decades of Driver Assistance Systems: Review and Future Perspectives

SafeVRU: A Research Platform for the Interaction of Self-Driving Vehicles with Vulnerable Road Users

Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation

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