Experience, Results and Lessons Learned from Automated Driving on Germany's Highways

Aeberhard, Michael; Rauch, Sebastian; Bahram, Mohammad; Tanzmeister, Georg; Thomas, Julian; Pilat, Yves; Homm, Florian; Huber, Werner; Kaempchen, Nico

doi:10.1109/mits.2014.2360306

Cited by 213 publications

(88 citation statements)

References 37 publications

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“…BMW, in [268], gives an interesting overview of their experience with autonomous driving over thousands of kilometers on public roads (highways). Their approach relies on lane marking using vision and laser, as well as odometry and a DGPS.…”

Section: Vehiclesmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

703

366

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Abstract-In this article, we propose a survey of the Simultaneous Localization And Mapping field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multi-vehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various largescale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

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Section: Vehiclesmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

703

366

View full text Add to dashboard Cite

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“…Relationships between Based on the above definitions, for a given traffic scenario, each entity can be contextualized as one individual. The map-related instances were extracted from the high-resolution digital map [32] and assigned an identification number (as Figure 3 shows). Instances of traffic participants were built based on perception data.…”

Section: Sidewalk Hassidewalk Junction Trafficsmentioning

confidence: 99%

A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

Geng

Liang

et al. 2017

Applied Sciences

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Driving through dynamically changing traffic scenarios is a highly challenging task for autonomous vehicles, especially on urban roadways. Prediction of surrounding vehicles' driving behaviors plays a crucial role in autonomous vehicles. Most traditional driving behavior prediction models work only for a specific traffic scenario and cannot be adapted to different scenarios. In addition, priori driving knowledge was never considered sufficiently. This study proposes a novel scenario-adaptive approach to solve these problems. A novel ontology model was developed to model traffic scenarios. Continuous features of driving behavior were learned by Hidden Markov Models (HMMs). Then, a knowledge base was constructed to specify the model adaptation strategies and store priori probabilities based on the scenario's characteristics. Finally, the target vehicle's future behavior was predicted considering both a posteriori probabilities and a priori probabilities. The proposed approach was sufficiently evaluated with a real autonomous vehicle. The application scope of traditional models can be extended to a variety of scenarios, while the prediction performance can be improved by the consideration of priori knowledge. For lane-changing behaviors, the prediction time horizon can be extended by up to 56% (0.76 s) on average. Meanwhile, long-term prediction precision can be enhanced by over 26%.

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“…3. Qualitative analysis of test environments for automated driving in simple and complex structured environments (based on [2], [6], [1], [9], [4], [11]) selection of these test criteria are used to rate existing test environments for the use in test scenarios with pedestrians.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…A classification in representative target groups might be advantageous. [2], [6], [1], [9], [4], [11], [12]) Fig. 5 summarizes a qualitative evaluation of existing test methods with important criteria.…”

Section: B Evaluation For Test Environments With Criteria In Collisimentioning

confidence: 99%

“Pedestrian in the loop”: An approach using virtual reality

Hartmann

Viehweger

Desmet

et al. 2017

2017 XXVI International Conference on Information, Communication and Automation Technologies (ICAT)

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Abstract-A large number of testing procedures have been developed to ensure vehicle safety in common and extreme driving situations. However, these conventional testing procedures are insufficient for testing autonomous vehicles. They have to handle unexpected scenarios with the same or less risk a human driver would take. Currently, safety related systems are not adequately tested, e.g. in collision avoidance scenarios with pedestrians. Examples are the change of pedestrian behaviour caused by interaction, environmental influences and personal aspects, which cannot be tested in real environments. It is proposed to use Virtual Reality techniques. This method can be seen as a new Pedestrian in the Loop testing procedure.

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Experience, Results and Lessons Learned from Automated Driving on Germany's Highways

Cited by 213 publications

References 37 publications

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

“Pedestrian in the loop”: An approach using virtual reality

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