One of the greatest challenges towards fully autonomous cars is the understanding of complex and dynamic scenes. Such understanding is needed for planning of maneuvers, especially those that are particularly frequent such as lane changes. While in recent years advanced driver-assistance systems have made driving safer and more comfortable, these have mostly focused on car following scenarios, and less on maneuvers involving lane changes. In this work we propose a situation assessment algorithm for classifying driving situations with respect to their suitability for lane changing. For this, we propose a deep learning architecture based on a Bidirectional Recurrent Neural Network, which uses Long Short-Term Memory units, and integrates a prediction component in the form of the Intelligent Driver Model. We prove the feasibility of our algorithm on the publicly available NGSIM datasets, where we outperform existing methods.
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@cite_5: Automated driving within a lane is a fascinating experience already. However, more exiting but also technically more challenging is to dare the next step of automating tactical behavior decisions for lane changes, as well. In this paper, we present our approach for situation assessment in tactical behavior planning for lane changes, whether lane changes are beneficial and or possible. We present a way to tackle perception uncertainties and how to monitor the system's abilities and current skills. This is achieved by a dynamic Bayesian network and an unscented variance transform. Our approach is evaluated not only in a simulation, but also in real traffic. Our implementation has recently been demonstrated to the public in the Audi A7 piloted driving concept vehicle, driving 550 miles from Stanford to Las Vegas to the Consumer Electronics Show (CES) 2015.
@cite_12: In this paper, we consider the problem of autonomous lane changing for self driving vehicles in a multi-lane, multi-agent setting. We present a framework that demonstrates a more structured and data efficient alternative to end-to-end complete policy learning on problems where the high-level policy is hard to formulate using traditional optimization or rule based methods but well designed low-level controllers are available. Our framework uses deep reinforcement learning solely to obtain a high-level policy for tactical decision making, while still maintaining a tight integration with the low-level controller, thus getting the best of both worlds. We accomplish this with Q-masking, a technique with which we are able to incorporate prior knowledge, constraints, and information from a low-level controller, directly in to the learning process thereby simplifying the reward function and making learning faster and data efficient. We provide preliminary results in a simulator and show our approach to be more efficient than a greedy baseline, and more successful and safer than human driving.
@cite_11: One important aspect of autonomous driving lies in the selection of maneuver sequences. Here the challenge is to optimize the driving comfort and travel-duration, while always keeping within the safety limits. Human drivers analyze and try to anticipate the traffic situation choosing their actions not only based on current information but also based on experience. The decision making process can be treated as a planning problem. Classical planning systems consider the autonomous driving task as a global numeric optimization problem, which in populated dynamic environments can become computationally intractable. In addition, purely numeric computations hamper the understanding of the decision making for the human user. We propose a planning system that presents a multi-level architecture, similar to the human reasoning process, which combines continuous planning with semantic information. This allows the planning system to deal with the complexity of the problem in a computationally efficient way and also provides an intuitive interface to communicate the decisions to the driver. We validate our approach in simulation and through a set of experiments carried out with a real vehicle and an integrated traffic simulation also known as vehicle in the loop (VIL).
In recent years first partially feasible algorithms for fully self-driving cars, capable of executing lane changes, have been developed and tested on the road. Mostly these were rule-based systems. A probabilistic framework for decision making was developed by and tested during autonomous drives on highways @cite_0 . Ulbrich and Maurer @cite_5 used Dynamic Bayesian Networks (DBNs) to evaluate situations to answer the question whether they are feasible and beneficial with respect to lane changes. Men ' e ndez- introduced a multi-level planning framework which makes use of semantic and numeric reasoning @cite_11 . used deep reinforcement learning for making lane change decisions @cite_12 .