This work examines the role of reinforcement learning in reducing the severity of on-road collisions by controlling velocity and steering in situations in which contact is imminent. We construct a model, given camera images as input, that is capable of learning and predicting the dynamics of obstacles, cars and pedestrians, and train our policy using this model. Two policies that control both braking and steering are compared against a baseline where the only action taken is (conventional) braking in a straight line. The two policies are trained using two distinct reward structures, one where any and all collisions incur a fixed penalty, and a second one where the penalty is calculated based on already established delta-v models of injury severity. The results show that both policies exceed the performance of the baseline, with the policy trained using injury models having the highest performance.
As opposed to most previous research, @cite_7 propose a model-free collision avoidance system using Deep Reinforcement Learning (DRL). They derive a balanced reward function for an autonomous braking system based on DRL, where the action space allows 4 choices: no braking, weak braking, medium and strong. The reward function consists of one component that penalizes the agent for braking too early while the second one is a penalty for collision with the pedestrian and takes into account the velocity of the vehicle in order to reflect the degree of damage. Due to an unstable learning performance (collisions rarely occuring), the authors use memory replay @cite_1 in order to remind the agent of collisions, whatever the present policy. The results show that collision rates are 0 for time-to-collision larger or equal to 1.5 seconds. This prompts us to study TTC values below @math .
@cite_1 @cite_7
2586886183 2050410119 2790797399 2800978878
In this paper, we propose a new autonomous braking system based on deep reinforcement learning. The proposed autonomous braking system automatically decides whether to apply the brake at each time step when confronting the risk of collision using the information on the obstacle obtained by the sensors. The problem of designing brake control is formulated as searching for the optimal policy in Markov decision process (MDP) model where the state is given by the relative position of the obstacle and the vehicle's speed, and the action space is defined as whether brake is stepped or not. The policy used for brake control is learned through computer simulations using the deep reinforcement learning method called deep Q-network (DQN). In order to derive desirable braking policy, we propose the reward function which balances the damage imposed to the obstacle in case of accident and the reward achieved when the vehicle runs out of risk as soon as possible. DQN is trained for the scenario where a vehicle is encountered with a pedestrian crossing the urban road. Experiments show that the control agent exhibits desirable control behavior and avoids collision without any mistake in various uncertain environments. A novel algorithm considering both time-to-collision (TTC) and safety braking distance for forward collision warning system is presented to alert and to assist a driver in keeping safety braking distance to avoid the collision accident in highway. We use the Artificial Neural Network (ANN) to predict the safety braking distance and TCC based on the most important parameters, which are the distance between the driving car and the vehicle (obstacle) ahead, the variable of the distance between the driving car and the vehicle (obstacle) ahead, vehicle weight, vehicle speed, slope of road, condition of road surface and the age of driver. The system compares the distance of vehicle (obstacle) ahead and safety braking distance and also determines whether the moving vehicle’s safety braking distance is enough or not. The reaction time of driver and pressure build-up time of braking system are all taken into account. The useful alert messages can serve as a safety assistance system for safer driving in highway. Existing research studies on vision and language grounding for robot navigation focus on improving model-free deep reinforcement learning (DRL) models in synthetic environments. However, model-free DRL models do not consider the dynamics in the real-world environments, and they often fail to generalize to new scenes. In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices---We propose a novel, planned-ahead hybrid reinforcement learning model that combines model-free and model-based reinforcement learning to solve a real-world vision-language navigation task. Our look-ahead module tightly integrates a look-ahead policy model with an environment model that predicts the next state and the reward. Experimental results suggest that our proposed method significantly outperforms the baselines and achieves the best on the real-world Room-to-Room dataset. Moreover, our scalable method is more generalizable when transferring to unseen environments, and the relative success rate is increased by 15.5 on the unseen test set. Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed, without the use of a 3D Lidar.