Autonomous braking system via deep reinforcement learning

By Hyunmin Chae, Chang Mook Kang, Byeoungdo Kim, Jaekyum Kim, Chung Choo Chung, Jun Won Choi. 2017

140 citations recorded by the source corpus.

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 the set of the brake actions including 1) no braking, 2) weak, 3) mid, 4) strong brakiong actions. 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.

Subfields: Artificial Intelligence, Automotive Engineering, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yasin Yilmaz
2. Ammar Haydari
3. Ibrahim Sobh
4. B. R. Kiran
5. Patrick Pérez
6. Guozhen Tan
7. Richard Bowden
8. Xin Xu

In Automotive Engineering

1. Yasin Yilmaz
2. Ammar Haydari
3. Ibrahim Sobh
4. B. R. Kiran
5. Patrick Pérez
6. Richard Bowden
7. Xin Xu
8. Phil Barber

In Control and Systems Engineering

1. Ibrahim Sobh
2. B. R. Kiran
3. Patrick Pérez
4. Richard Bowden
5. Xin Xu
6. Phil Barber
7. Chao Yu
8. Yaochu Jin

Citations in the corpus

• Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, background): “Authors in [122] design a second "trauma" replay memory that contains only collision situations in order to pool positive and negative”
• Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs (2020, background): “braking system control [12], ramp-metering [13], autonomous intersection management [14], and lane keeping/changing”
• Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey (2020, background): “[117] DQN Autonomous breaking Pedestrian detection PreScan vehicle simulator Without Trauma memory”
• Deep Reinforcement Learning for Autonomous Driving (2018, background): “Recent works [22, 4, 6] are mainly focus on deep reinforcement learning paradigm to achieve autonomous driving.”
• A Survey of Deep Learning Applications to Autonomous Vehicle Control (2019, methodology): “[111], who proposed an autonomous braking”