Trajectory planning for Bertha &amp;#x2014; A local, continuous method

Ziegler, Julius; Bender, Philipp; Dang, Thao; Stiller, Christoph

doi:10.1109/ivs.2014.6856581

Cited by 407 publications

(246 citation statements)

References 13 publications

Supporting

Mentioning

224

Contrasting

Unclassified

Order By: Relevance

“…Ziegler et al [8] presented a local optimization based trajectory generation framework that was used to successfully drive the Bertha-Benz-Memorial-Route fully autonomously. The algorithm solves the problem directly in continuous space with a cost function that incorporates several comfort and distance cost terms and dynamic obstacles are handled by creating polygon shaped constraints at different time instances along the trajectory.…”

Section: A Related Workmentioning

confidence: 99%

Interaction aware trajectory planning for merge scenarios in congested traffic situations

Evestedt

Ward²,

Folkesson³

et al. 2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

Abstract-In many traffic situations there are times where interaction with other drivers is necessary and unavoidable in order to safely progress towards an intended destination. This is especially true for merge manoeuvres into dense traffic, where drivers sometimes must be somewhat aggressive and show the intention of merging in order to interact with the other driver and make the driver open the gap needed to execute the manoeuvre safely. Many motion planning frameworks for autonomous vehicles adopt a reactive approach where simple models of other traffic participants are used and therefore need to adhere to large margins in order to behave safely. However, the large margins needed can sometimes get the system stuck in congested traffic where time gaps between vehicles are too small. In other situations, such as a highway merge, it can be significantly more dangerous to stop on the entrance ramp if the gaps are found to be too small than to make a slightly more aggressive manoeuvre and let the driver behind open the gap needed. To remedy this problem, this work uses the Intelligent Driver Model (IDM) to explicitly model the interaction of other drivers and evaluates the risk by their required deceleration in a similar manner as the Minimum Overall Breaking Induced by Lane change (MOBIL) model that has been used in large scale traffic simulations before. This allows the algorithm to evaluate the effect on other drivers depending on our own trajectory plans by simulating the nearby traffic situation. Finding a globally optimal solution is often intractable in these situations so instead a large set of candidate trajectories are generated that are evaluated against the traffic scene by forward simulations of other traffic participants. By discretization and using an efficient trajectory generator together with efficient modelling of the traffic scene real-time demands can be met.

show abstract

Section: A Related Workmentioning

confidence: 99%

Interaction aware trajectory planning for merge scenarios in congested traffic situations

Evestedt

Ward²,

Folkesson³

et al. 2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

show abstract

“…Also, in the highly dynamic scene, a trajectory planning horizon of about 3 s can result in a rather reactive behavior [1]. Thus, to ensure a safe vehicle sate at the end of the planned trajectory with higher speed a planning horizon of about 10 s was used in [2]. In order to keep the plan executable in the future, an estimate of the upcoming situation is important.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic long-term prediction for autonomous vehicles

Hoermann

Stumper

Dietmayer

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Abstract-Long-term prediction of traffic participants is crucial to enable autonomous driving on public roads. The quality of the prediction directly affects the frequency of trajectory planning. With a poor estimation of the future development, more computational effort has to be put in re-planning, and a safe vehicle state at the end of the planning horizon is not guaranteed. A holistic probabilistic prediction, considering inputs, results and parameters as random variables, highly reduces the problem. A time frame of several seconds requires a probabilistic description of the scene evolution, where uncertainty or accuracy is represented by the trajectory distribution. Following this strategy, a novel evaluation method is needed, coping with the fact, that the future evolution of a scene is also uncertain. We present a method to evaluate the probabilistic prediction of real traffic scenes with varying start conditions. The proposed prediction is based on a particle filter, estimating behavior describing parameters of a microscopic traffic model. Experiments on real traffic data with random leading vehicles show the applicability in terms of convergence, enabling long-term prediction using forward propagation.

show abstract

“…As a result real time capability is not an issue. Nevertheless more complex and adaptable trajectory planning algorithms have been developed, see for example [7], [8] or [9]. More recently the two tasks trajectory planning and vehicle control have been united by model predictive approaches in [10], [11], [12] and [13].…”

Section: Introductionmentioning

confidence: 99%