| Definition | Highway-env is a versatile simulation env for developing and testing autonomous driving algorithms in diverse traffic scenarios. |
| No of environments | 326 |
Highway-env is a dynamic simulation environment crafted for the exploration of autonomous driving and tactical decision-making strategies. Developed by Edouard Leurent, this innovative framework immerses agents in a variety of realistic driving scenarios, providing a rich platform for reinforcement learning applications. At its core, the highway-v0 environment places the ego-vehicle on a multilane highway populated with other vehicles. The agent’s primary objective is to accelerate safely while avoiding collisions, which encourages the adoption of safe driving behaviors, such as adhering to lane discipline. For those looking to expedite training, a faster variant of the environment is available, trading off some simulation accuracy for improved performance.
The highway-env framework encompasses a diverse array of driving challenges designed to enhance agents’ decision-making capabilities. In the Merge scenario, agents navigate a junction where they must balance maintaining speed with allowing incoming vehicles to merge safely into traffic. The Roundabout environment presents additional complexities as agents tackle flowing traffic, focusing on lane changes and efficient navigation through the roundabout. In contrast, the Parking task challenges agents to accurately park in designated spaces while managing their vehicle’s orientation and positioning.
Moreover, the Intersection environment tests agents’ skills in negotiating busy intersections, requiring them to maneuver around competing vehicles with precision. The Racetrack environment emphasizes high-speed driving, demanding lane-keeping and obstacle avoidance skills to ensure safety while navigating the course.
The flexibility of the highway-env environment enables it to support various reinforcement learning algorithms, allowing agents to learn and adapt through their simulated experiences in these diverse scenarios. This makes it an invaluable resource for researchers and developers focused on the development of autonomous navigation technologies.
As a crucial tool for advancing self-driving technologies, highway-env offers realistic driving environments that facilitate the exploration and refinement of strategies applicable to the rapidly evolving field of autonomous vehicles.
| Example observations | Observed (other) vehicles: number, order, features range, obstacles. etc. |
| Example actions | Accelerate, steer. |
| Example reward | collision, right lane, reach goal, lane centering etc. |