Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side by side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

2005년 대한 생화학·분자생물학회 하계학술대회를 다녀와서

Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side-by-side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.

A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles

With the objective to improve road safety, the automotive industry is moving toward more “intelligent” vehicles. One of the major challenges is to detect dangerous situations and react accordingly in order to avoid or mitigate accidents. This requires predicting the likely evolution of the current traffic situation, and assessing how dangerous that future situation might be. This paper is a survey of existing methods for motion prediction and risk assessment for intelligent vehicles. The proposed classification is based on the semantics used to define motion and risk. We point out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model.

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A survey on motion prediction and risk assessment for intelligent vehicles

Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic. Motion of all vehicles in a scene is governed by the traffic context, i.e., the motion and relative spatial configuration of neighboring vehicles. In this paper we propose an LSTM encoder-decoder model that uses convolutional social pooling as an improvement to social pooling layers for robustly learning interdependencies in vehicle motion. Additionally, our model outputs a multi-modal predictive distribution over future trajectories based on maneuver classes. We evaluate our model using the publicly available NGSIM US-101 and I-80 datasets. Our results show improvement over the state of the art in terms of RMS values of prediction error and negative log-likelihoods of true future trajectories under the model's predictive distribution. We also present a qualitative analysis of the model's predicted distributions for various traffic scenarios.

/pdf/convolutional-social-pooling-for-vehicle-trajectory-4fzlp3b04v.pdf

Convolutional Social Pooling for Vehicle Trajectory Prediction

This paper presents a model-based algorithm that estimates how the driver of a vehicle can either steer, brake, or accelerate to avoid colliding with an arbitrary object. In this algorithm, the motion of the vehicle is described by a linear bicycle model, and the perimeter of the vehicle is represented by a rectangle. The estimated perimeter of the object is described by a polygon that is allowed to change size, shape, position, and orientation at sampled time instances. Potential evasive maneuvers are modeled, parameterized, and approximated such that an analytical expression can be derived to estimate the set of maneuvers that the driver can use to avoid a collision. This set of maneuvers is then assessed to determine if the driver needs immediate assistance to avoid or mitigate an accident. The proposed threat-assessment algorithm is evaluated using authentic data from both real traffic conditions and collision situations on a test track and by using simulations with a detailed vehicle model. The evaluations show that the algorithm outperforms conventional threat-assessment algorithms at rear-end collisions in terms of the timing of autonomous brake activation. This is crucial for increasing the performance of collision-avoidance systems and for decreasing the risk of unnecessary braking. Moreover, the algorithm is computationally efficient and can be used to assist the driver in avoiding or mitigating collisions with all types of road users in all kinds of traffic scenarios.

Model-Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions

Advanced driver assistance systems or highly automated driving systems for lane change maneuvers are expected to enhance highway traffic safety, transport efficiency, and driver comfort. To extend the capability of current advanced driver assistance systems, and eventually progress to highly automated highway driving, the task of automatically determine if, when, and how to perform a lane change maneuver, is essential. This paper thereby presents a low-complexity lane change maneuver algorithm which determines whether a lane change maneuver is desirable, and if so, selects an appropriate inter-vehicle traffic gap and time instance to perform the maneuver, and calculates the corresponding longitudinal and lateral control trajectory. The ability of the proposed lane change maneuver algorithm to make appropriate maneuver decisions and generate smooth and safe lane change trajectories in various traffic situations is demonstrated by simulation and experimental results.

If, When, and How to Perform Lane Change Maneuvers on Highways

By considering a lane change maneuver as primarily a longitudinal motion planning problem, this paper presents a lane change maneuver algorithm with a pragmatic approach to determine an inter-vehicle traffic gap and time instance to perform the maneuver. The proposed approach selects an appropriate inter-vehicle traffic gap and time instance to perform the lane change maneuver by simply estimating whether there might exist a longitudinal trajectory that allows the automated vehicle to safely perform the maneuver. The lane change maneuver algorithm then proceeds to solve two loosely coupled convex quadratic programs to obtain the longitudinal trajectory to position the automated vehicle in the selected inter-vehicle traffic gap at the desired time instance and the corresponding lateral trajectory. Simulation results demonstrate the capability of the proposed approach to select an appropriate inter-vehicle traffic gap and time instance to initialize the lateral motion of a lane change maneuver in various traffic scenarios. The real-time ability of the lane change maneuver algorithm to generate safe and smooth trajectories is shown by experimental results of a Volvo V60 performing automated lane change maneuvers on a test track.

Lane Change Maneuvers for Automated Vehicles

A vehicle, vehicle system and method for increasing at least one of safety and comfort during autonomous driving is provided. The vehicle system includes an autonomous drive arrangement with multiple sensors, a vehicle control arrangement and a positioning system. The vehicle system is configured to determine an estimated probability that at least one sensor will become unavailable, or an estimated time/distance ahead until at least one sensor is determined to become unavailable. The vehicle system is further configured to activate at least one countermeasure based on at least one of the estimated probability, the estimated time and the estimated distance.

Vehicle, vehicle system and method for increasing safety and/or comfort during autonomous driving

Automated driving is predicted to enhance traffic safety, transport efficiency, and driver comfort. To extend the capability of current advanced driver assistance systems, and eventually realize fully automated driving, the intelligent vehicle system must have the ability to plan different maneuvers while adapting to the surrounding traffic environment. This paper presents an algorithm for longitudinal and lateral trajectory planning for automated driving maneuvers where the vehicle does not have right of way, i.e., yielding maneuvers. Such maneuvers include, e.g., lane change, roundabout entry, and intersection crossing. In the proposed approach, the traffic environment which the vehicle must traverse is incorporated as constraints on its longitudinal and lateral positions. The trajectory planning problem can thereby be formulated as two loosely coupled low-complexity model predictive control problems for longitudinal and lateral motion. Simulation results demonstrate the ability of the proposed trajectory planning algorithm to generate smooth collision-free maneuvers which are appropriate for various traffic situations.

Mattias Brännström

Papers

Model-Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions

If, When, and How to Perform Lane Change Maneuvers on Highways

Lane Change Maneuvers for Automated Vehicles

Vehicle, vehicle system and method for increasing safety and/or comfort during autonomous driving

Longitudinal and Lateral Control for Automated Yielding Maneuvers