Radar contribution to highly automated driving

Dickmann, Juergen; Appenrodt, Nils; Bloecher, Hans-Ludwig; Brenk, Carsten; Hackbarth, T.; Hahn, Markus; Klappstein, Jens; Muntzinger, Marc; Sailer, Alfons

doi:10.1109/eurad.2014.6991295

Cited by 20 publications

(19 citation statements)

References 9 publications

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“…The Radio Detection and Ranging System (RADAR) is equipped with short-range and long-range sensors. Since its invention in the early 1930s, radar technology has inspired a considerable amount of inventions of every day impact [60]. Light Detection and Ranging (LIDAR) is an on-board sensor used in obtaining information about the environment to ensure that the autonomous vehicle's dynamical safety is translated to avoid single tire lift-off [61].…”

Section: Environment St St+1mentioning

confidence: 99%

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

et al. 2020

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Steering angle prediction is critical in the control of Autonomous Vehicles (AVs) and has attracted the attention of researchers, manufacturers, and insurance companies in the automotive industry. Different Deep Learning (DL) architectures have been applied to predict the steering angle of AVs in various scenarios. A survey on steering angle prediction based on deep learning algorithms can help expert researchers identify those areas that require development. Also, novice researchers can use the survey as a starting point. In this paper, we present a broad study on the recent advances made in DL architectures that covers the steering angle prediction of AVs. A new comprehensive taxonomy of the application of DL in steering angle prediction of AVs is created. The survey presents a concise research summary synthesis, and analysis. It is found that most researchers depend on Convolutional Neural Network (CNN) over other DL architectures in predicting the steering angle of autonomous driving vehicles. Also identified are open research problems. The prominent challenge facing DL-based steering angle prediction of AVs is lack of sufficient real-world datasets, which means that researchers largely depend on data generated from simulated environments. Lastly, alternative viewpoints to solve the identified open research challenges are proposed, pointing towards promising future research directions.

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Section: Environment St St+1mentioning

confidence: 99%

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

et al. 2020

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“…, and are the longitudinal velocity, longitudinal acceleration and heading angle of moving obstacles, respectively. When the real-time position of the obstacle is already available in (32), the cost function of the MP algorithm when the moving obstacle is considered can be presented the same as the cost function in equation (29).…”

Section: Definition and Representation Of Static And Dynamic Obstaclementioning

confidence: 99%

Integrated trajectory planning and control for obstacle avoidance manoeuvre using non‐linear vehicle MP algorithm

et al. 2018

IET Intelligent Transport Systems

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In the current literature, model-predictive (MP) algorithm is widely applied in autonomous vehicle trajectory planning and control but most of current studies only apply the linear tyre model which cannot accurately present the tyre non-linear characteristic. Furthermore, most of these studies separately consider the trajectory planning and trajectory control of autonomous vehicle and few of them have integrated the trajectory planning and trajectory control together. To fill in above research gaps, this study proposes the integrated trajectory planning and trajectory control method using nonlinear vehicle MP algorithm. In order to fully utilise the advantages of four-wheel-independent-steering (4WIS) and fourwheel-independent-driving (4WID) vehicle, the MP algorithm is proposed based on four wheel dynamics model and nonlinear Dugoff tyre model. This study also proposes the mathematic modelling of the static obstacle and dynamic obstacle for the obstacle avoidance manoeuvre of the autonomous vehicle. Finally, simulation results have been presented to show the effectiveness of proposed control method.

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“…For instance, a Mercedes-Benz S-Class vehicle equipped with six radar sensors covering the full 360° angular range of the environment around the vehicle in the near and far range has been introduced in 2013. The vehicle drove completely autonomous for about 100 km from Mannheim to Pforzheim, Germany, in normal traffic [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Steering Control, Speed Assistance and Localization for Inner-City Vehicles

Olivares-Méndez

Sánchez-López

Jiménez

et al. 2016

Sensors

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Autonomous route following with road vehicles has gained popularity in the last few decades. In order to provide highly automated driver assistance systems, different types and combinations of sensors have been presented in the literature. However, most of these approaches apply quite sophisticated and expensive sensors, and hence, the development of a cost-efficient solution still remains a challenging problem. This work proposes the use of a single monocular camera sensor for an automatic steering control, speed assistance for the driver and localization of the vehicle on a road. Herein, we assume that the vehicle is mainly traveling along a predefined path, such as in public transport. A computer vision approach is presented to detect a line painted on the road, which defines the path to follow. Visual markers with a special design painted on the road provide information to localize the vehicle and to assist in its speed control. Furthermore, a vision-based control system, which keeps the vehicle on the predefined path under inner-city speed constraints, is also presented. Real driving tests with a commercial car on a closed circuit finally prove the applicability of the derived approach. In these tests, the car reached a maximum speed of 48 km/h and successfully traveled a distance of 7 km without the intervention of a human driver and any interruption.

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Radar contribution to highly automated driving

Cited by 20 publications

References 9 publications

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

Integrated trajectory planning and control for obstacle avoidance manoeuvre using non‐linear vehicle MP algorithm

Vision-Based Steering Control, Speed Assistance and Localization for Inner-City Vehicles

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